Source code for MDAnalysis.coordinates.base

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# MDAnalysis --- https://www.mdanalysis.org
# Copyright (c) 2006-2017 The MDAnalysis Development Team and contributors
# (see the file AUTHORS for the full list of names)
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# Please cite your use of MDAnalysis in published work:
#
# 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.
# MDAnalysis: A Python package for the rapid analysis of molecular dynamics
# simulations. In S. Benthall and S. Rostrup editors, Proceedings of the 15th
# Python in Science Conference, pages 102-109, Austin, TX, 2016. SciPy.
#
# N. Michaud-Agrawal, E. J. Denning, T. B. Woolf, and O. Beckstein.
# MDAnalysis: A Toolkit for the Analysis of Molecular Dynamics Simulations.
# J. Comput. Chem. 32 (2011), 2319--2327, doi:10.1002/jcc.21787
#


"""\
Base classes --- :mod:`MDAnalysis.coordinates.base`
===================================================

Derive other Timestep, FrameIterator, Reader and Writer classes from the classes 
in this module. The derived classes must follow the :ref:`Trajectory API`
in :mod:`MDAnalysis.coordinates.__init__`.

Timestep
--------

A :class:`Timestep` holds information for the current time frame in
the trajectory. It is one of the central data structures in
MDAnalysis.

.. class:: Timestep

   .. automethod:: __init__
   .. automethod:: from_coordinates
   .. automethod:: from_timestep
   .. automethod:: _init_unitcell
   .. autoattribute:: n_atoms
   .. attribute::`frame`

      frame number (0-based)

      .. versionchanged:: 0.11.0
         Frames now 0-based; was 1-based

   .. autoattribute:: time
   .. autoattribute:: dt
   .. autoattribute:: positions
   .. autoattribute:: velocities
   .. autoattribute:: forces
   .. autoattribute:: has_positions
   .. autoattribute:: has_velocities
   .. autoattribute:: has_forces
   .. attribute:: _pos

      :class:`numpy.ndarray` of dtype :class:`~numpy.float32` of shape
      (*n_atoms*, 3) and internal FORTRAN order, holding the raw
      cartesian coordinates (in MDAnalysis units, i.e. Å).

      .. Note::

         Normally one does not directly access :attr:`_pos` but uses
         the :meth:`~MDAnalysis.core.groups.AtomGroup.coordinates`
         method of an :class:`~MDAnalysis.core.groups.AtomGroup` but
         sometimes it can be faster to directly use the raw
         coordinates. Any changes to this array are immediately
         reflected in atom positions. If the frame is written to a new
         trajectory then the coordinates are changed. If a new
         trajectory frame is loaded, then *all* contents of
         :attr:`_pos` are overwritten.

   .. attribute:: _velocities

      :class:`numpy.ndarray` of dtype :class:`~numpy.float32`. of shape
      (*n_atoms*, 3), holding the raw velocities (in MDAnalysis
      units, i.e. typically Å/ps).

      .. Note::

         Normally velocities are accessed through the
         :attr:`velocities` or the
         :meth:`~MDAnalysis.core.groups.AtomGroup.velocities`
         method of an :class:`~MDAnalysis.core.groups.AtomGroup`

         :attr:`~Timestep._velocities` only exists if the :attr:`has_velocities`
         flag is True

      .. versionadded:: 0.7.5

   .. attribute:: _forces

      :class:`numpy.ndarray` of dtype :class:`~numpy.float32`. of shape
      (*n_atoms*, 3), holding the forces

      :attr:`~Timestep._forces` only exists if :attr:`has_forces`
      is True

      .. versionadded:: 0.11.0
         Added as optional to :class:`Timestep`

   .. autoattribute:: dimensions
   .. autoattribute:: triclinic_dimensions
   .. autoattribute:: volume
   .. automethod:: __getitem__
   .. automethod:: __eq__
   .. automethod:: __iter__
   .. automethod:: copy
   .. automethod:: copy_slice


FrameIterators
--------------

Iterator classes used by the by the :class:`ProtoReader`.

.. autoclass:: FrameIteratorBase
    
.. autoclass:: FrameIteratorSliced
    
.. autoclass:: FrameIteratorAll
    
.. autoclass:: FrameIteratorIndices


Readers
-------

Readers know how to take trajectory data in a given format and present it in a
common API to the user in MDAnalysis. There are two types of readers:

1. Readers for *multi frame trajectories*, i.e., file formats that typically
   contain many frames. These readers are typically derived from
   :class:`ReaderBase`.

2. Readers for *single frame formats*: These file formats only contain a single
   coordinate set. These readers are derived from
   :class`:SingleFrameReaderBase`.

The underlying low-level readers handle closing of files in different
ways. Typically, the MDAnalysis readers try to ensure that files are always
closed when a reader instance is garbage collected, which relies on
implementing a :meth:`~ReaderBase.__del__` method. However, in some cases, this
is not necessary (for instance, for the single frame formats) and then such a
method can lead to undesirable side effects (such as memory leaks). In this
case, :class:`ProtoReader` should be used.


.. autoclass:: ReaderBase
   :members:
   :inherited-members:

.. autoclass:: SingleFrameReaderBase
   :members:
   :inherited-members:

.. autoclass:: ProtoReader
   :members:



Writers
-------

Writers know how to write information in a :class:`Timestep` to a trajectory
file.

.. autoclass:: WriterBase
   :members:
   :inherited-members:


Helper classes
--------------

The following classes contain basic functionality that all readers and
writers share.

.. autoclass:: IOBase
   :members:

"""
from __future__ import absolute_import
import six
from six.moves import range

import numpy as np
import numbers
import copy
import warnings
import weakref

from . import core
from .. import NoDataError
from .. import (
    _READERS,
    _SINGLEFRAME_WRITERS,
    _MULTIFRAME_WRITERS,
)
from .. import units
from ..auxiliary.base import AuxReader
from ..auxiliary.core import auxreader
from ..core import flags
from ..lib.util import asiterable, Namespace


[docs]class Timestep(object): """Timestep data for one frame :Methods: ``ts = Timestep(n_atoms)`` create a timestep object with space for n_atoms .. versionchanged:: 0.11.0 Added :meth:`from_timestep` and :meth:`from_coordinates` constructor methods. :class:`Timestep` init now only accepts integer creation. :attr:`n_atoms` now a read only property. :attr:`frame` now 0-based instead of 1-based. Attributes `status` and `step` removed. """ order = 'F'
[docs] def __init__(self, n_atoms, **kwargs): """Create a Timestep, representing a frame of a trajectory Parameters ---------- n_atoms : int The total number of atoms this Timestep describes positions : bool, optional Whether this Timestep has position information [``True``] velocities : bool (optional) Whether this Timestep has velocity information [``False``] forces : bool (optional) Whether this Timestep has force information [``False``] reader : Reader (optional) A weak reference to the owning Reader. Used for when attributes require trajectory manipulation (e.g. dt) dt : float (optional) The time difference between frames (ps). If :attr:`time` is set, then `dt` will be ignored. time_offset : float (optional) The starting time from which to calculate time (in ps) .. versionchanged:: 0.11.0 Added keywords for `positions`, `velocities` and `forces`. Can add and remove position/velocity/force information by using the ``has_*`` attribute. """ # readers call Reader._read_next_timestep() on init, incrementing # self.frame to 0 self.frame = -1 self._n_atoms = n_atoms self.data = {} for att in ('dt', 'time_offset'): try: self.data[att] = kwargs[att] except KeyError: pass try: # do I have a hook back to the Reader? self._reader = weakref.ref(kwargs['reader']) except KeyError: pass # Stupid hack to make it allocate first time round # ie we have to go from not having, to having positions # to make the Timestep allocate self._has_positions = False self._has_velocities = False self._has_forces = False # These will allocate the arrays if the has flag # gets set to True self.has_positions = kwargs.get('positions', True) self.has_velocities = kwargs.get('velocities', False) self.has_forces = kwargs.get('forces', False) self._unitcell = self._init_unitcell() # set up aux namespace for adding auxiliary data self.aux = Namespace()
[docs] @classmethod def from_timestep(cls, other, **kwargs): """Create a copy of another Timestep, in the format of this Timestep .. versionadded:: 0.11.0 """ ts = cls(other.n_atoms, positions=other.has_positions, velocities=other.has_velocities, forces=other.has_forces, **kwargs) ts.frame = other.frame ts.dimensions = other.dimensions try: ts.positions = other.positions.copy(order=cls.order) except NoDataError: pass try: ts.velocities = other.velocities.copy(order=cls.order) except NoDataError: pass try: ts.forces = other.forces.copy(order=cls.order) except NoDataError: pass # Optional attributes that don't live in .data # should probably iron out these last kinks for att in ('_frame',): try: setattr(ts, att, getattr(other, att)) except AttributeError: pass if hasattr(ts, '_reader'): other._reader = weakref.ref(ts._reader()) ts.data = copy.deepcopy(other.data) return ts
[docs] @classmethod def from_coordinates(cls, positions=None, velocities=None, forces=None, **kwargs): """Create an instance of this Timestep, from coordinate data Can pass position, velocity and force data to form a Timestep. .. versionadded:: 0.11.0 """ has_positions = positions is not None has_velocities = velocities is not None has_forces = forces is not None lens = [len(a) for a in [positions, velocities, forces] if a is not None] if not lens: raise ValueError("Must specify at least one set of data") n_atoms = max(lens) # Check arrays are matched length? if not all(val == n_atoms for val in lens): raise ValueError("Lengths of input data mismatched") ts = cls(n_atoms, positions=has_positions, velocities=has_velocities, forces=has_forces, **kwargs) if has_positions: ts.positions = positions if has_velocities: ts.velocities = velocities if has_forces: ts.forces = forces return ts
[docs] def _init_unitcell(self): """Create custom datastructure for :attr:`_unitcell`.""" # override for other Timesteps return np.zeros((6), np.float32)
[docs] def __eq__(self, other): """Compare with another Timestep .. versionadded:: 0.11.0 """ if not isinstance(other, Timestep): return False if not self.frame == other.frame: return False if not self.n_atoms == other.n_atoms: return False if not self.has_positions == other.has_positions: return False if self.has_positions: if not (self.positions == other.positions).all(): return False if not self.has_velocities == other.has_velocities: return False if self.has_velocities: if not (self.velocities == other.velocities).all(): return False if not self.has_forces == other.has_forces: return False if self.has_forces: if not (self.forces == other.forces).all(): return False return True
def __ne__(self, other): return not self == other
[docs] def __getitem__(self, atoms): """Get a selection of coordinates ``ts[i]`` return coordinates for the i'th atom (0-based) ``ts[start:stop:skip]`` return an array of coordinates, where start, stop and skip correspond to atom indices, :attr:`MDAnalysis.core.groups.Atom.index` (0-based) """ if isinstance(atoms, numbers.Integral): return self._pos[atoms] elif isinstance(atoms, (slice, np.ndarray)): return self._pos[atoms] else: raise TypeError
def __len__(self): return self.n_atoms
[docs] def __iter__(self): """Iterate over coordinates ``for x in ts`` iterate of the coordinates, atom by atom """ for i in range(self.n_atoms): yield self[i]
def __repr__(self): desc = "< Timestep {0}".format(self.frame) try: tail = " with unit cell dimensions {0} >".format(self.dimensions) except NotImplementedError: tail = " >" return desc + tail
[docs] def copy(self): """Make an independent ("deep") copy of the whole :class:`Timestep`.""" return self.__deepcopy__()
def __deepcopy__(self): return self.from_timestep(self)
[docs] def copy_slice(self, sel): """Make a new `Timestep` containing a subset of the original `Timestep`. Parameters ---------- sel : array_like or slice The underlying position, velocity, and force arrays are sliced using a :class:`list`, :class:`slice`, or any array-like. Returns ------- :class:`Timestep` A `Timestep` object of the same type containing all header information and all atom information relevant to the selection. Note ---- The selection must be a 0 based :class:`slice` or array of the atom indices in this :class:`Timestep` Example ------- Using a Python :class:`slice` object:: new_ts = ts.copy_slice(slice(start, stop, step)) Using a list of indices:: new_ts = ts.copy_slice([0, 2, 10, 20, 23]) .. versionadded:: 0.8 .. versionchanged:: 0.11.0 Reworked to follow new Timestep API. Now will strictly only copy official attributes of the Timestep. """ # Detect the size of the Timestep by doing a dummy slice try: pos = self.positions[sel, :] except NoDataError: # It's cool if there's no Data, we'll live pos = None except: raise TypeError("Selection type must be compatible with slicing" " the coordinates") try: vel = self.velocities[sel, :] except NoDataError: vel = None except: raise TypeError("Selection type must be compatible with slicing" " the coordinates") try: force = self.forces[sel, :] except NoDataError: force = None except: raise TypeError("Selection type must be compatible with slicing" " the coordinates") new_TS = self.__class__.from_coordinates( positions=pos, velocities=vel, forces=force) new_TS._unitcell = self._unitcell.copy() new_TS.frame = self.frame for att in ('_frame',): try: setattr(new_TS, att, getattr(self, att)) except AttributeError: pass if hasattr(self, '_reader'): new_TS._reader = weakref.ref(self._reader()) new_TS.data = copy.deepcopy(self.data) return new_TS
@property def n_atoms(self): """A read only view of the number of atoms this Timestep has .. versionchanged:: 0.11.0 Changed to read only property """ # In future could do some magic here to make setting n_atoms # resize the coordinate arrays, but # - not sure if that is ever useful # - not sure how to manage existing data upon extension return self._n_atoms @property def has_positions(self): """A boolean of whether this Timestep has position data This can be changed to ``True`` or ``False`` to allocate space for or remove the data. .. versionadded:: 0.11.0 """ return self._has_positions @has_positions.setter def has_positions(self, val): if val and not self._has_positions: # Setting this will always reallocate position data # ie # True -> False -> True will wipe data from first True state self._pos = np.zeros((self.n_atoms, 3), dtype=np.float32, order=self.order) self._has_positions = True elif not val: # Unsetting val won't delete the numpy array self._has_positions = False @property def positions(self): """A record of the positions of all atoms in this Timestep Setting this attribute will add positions to the Timestep if they weren't originally present. Returns ------- positions : numpy.ndarray with dtype numpy.float32 position data of shape ``(n_atoms, 3)`` for all atoms Raises ------ :exc:`MDAnalysis.exceptions.NoDataError` if the Timestep has no position data .. versionchanged:: 0.11.0 Now can raise :exc:`NoDataError` when no position data present """ if self.has_positions: return self._pos else: raise NoDataError("This Timestep has no positions") @positions.setter def positions(self, new): self.has_positions = True self._pos[:] = new @property def _x(self): """A view onto the x dimension of position data .. versionchanged:: 0.11.0 Now read only """ return self.positions[:, 0] @property def _y(self): """A view onto the y dimension of position data .. versionchanged:: 0.11.0 Now read only """ return self.positions[:, 1] @property def _z(self): """A view onto the z dimension of position data .. versionchanged:: 0.11.0 Now read only """ return self.positions[:, 2] @property def has_velocities(self): """A boolean of whether this Timestep has velocity data This can be changed to ``True`` or ``False`` to allocate space for or remove the data. .. versionadded:: 0.11.0 """ return self._has_velocities @has_velocities.setter def has_velocities(self, val): if val and not self._has_velocities: self._velocities = np.zeros((self.n_atoms, 3), dtype=np.float32, order=self.order) self._has_velocities = True elif not val: self._has_velocities = False @property def velocities(self): """A record of the velocities of all atoms in this Timestep Setting this attribute will add velocities to the Timestep if they weren't originally present. Returns ------- velocities : numpy.ndarray with dtype numpy.float32 velocity data of shape ``(n_atoms, 3)`` for all atoms Raises ------ :exc:`MDAnalysis.exceptions.NoDataError` if the Timestep has no velocity data .. versionadded:: 0.11.0 """ if self.has_velocities: return self._velocities else: raise NoDataError("This Timestep has no velocities") @velocities.setter def velocities(self, new): self.has_velocities = True self._velocities[:] = new @property def has_forces(self): """A boolean of whether this Timestep has force data This can be changed to ``True`` or ``False`` to allocate space for or remove the data. .. versionadded:: 0.11.0 """ return self._has_forces @has_forces.setter def has_forces(self, val): if val and not self._has_forces: self._forces = np.zeros((self.n_atoms, 3), dtype=np.float32, order=self.order) self._has_forces = True elif not val: self._has_forces = False @property def forces(self): """A record of the forces of all atoms in this Timestep Setting this attribute will add forces to the Timestep if they weren't originally present. Returns ------- forces : numpy.ndarray with dtype numpy.float32 force data of shape ``(n_atoms, 3)`` for all atoms Raises ------ :exc:`MDAnalysis.exceptions.NoDataError` if the Timestep has no force data .. versionadded:: 0.11.0 """ if self.has_forces: return self._forces else: raise NoDataError("This Timestep has no forces") @forces.setter def forces(self, new): self.has_forces = True self._forces[:] = new @property def dimensions(self): """unitcell dimensions (*A*, *B*, *C*, *alpha*, *beta*, *gamma*) lengths *a*, *b*, *c* are in the MDAnalysis length unit (Å), and angles are in degrees. Setting dimensions will populate the underlying native format description of box dimensions """ # The actual Timestep._unitcell depends on the underlying # trajectory format. It can be e.g. six floats representing # the box edges and angles or the 6 unique components of the # box matrix or the full box matrix. return self._unitcell @dimensions.setter def dimensions(self, box): self._unitcell[:] = box @property def volume(self): """volume of the unitcell""" return core.box_volume(self.dimensions) @property def triclinic_dimensions(self): """The unitcell dimensions represented as triclinic vectors Returns ------- numpy.ndarray A (3, 3) numpy.ndarray of unit cell vectors Examples -------- The unitcell for a given system can be queried as either three vectors lengths followed by their respective angle, or as three triclinic vectors. >>> ts.dimensions array([ 13., 14., 15., 90., 90., 90.], dtype=float32) >>> ts.triclinic_dimensions array([[ 13., 0., 0.], [ 0., 14., 0.], [ 0., 0., 15.]], dtype=float32) Setting the attribute also works:: >>> ts.triclinic_dimensions = [[15, 0, 0], [5, 15, 0], [5, 5, 15]] >>> ts.dimensions array([ 15. , 15.81138802, 16.58312416, 67.58049774, 72.45159912, 71.56504822], dtype=float32) See Also -------- :func:`MDAnalysis.lib.mdamath.triclinic_vectors` .. versionadded:: 0.11.0 """ return core.triclinic_vectors(self.dimensions) @triclinic_dimensions.setter def triclinic_dimensions(self, new): """Set the unitcell for this Timestep as defined by triclinic vectors .. versionadded:: 0.11.0 """ self.dimensions = core.triclinic_box(*new) @property def dt(self): """The time difference in ps between timesteps Note ---- This defaults to 1.0 ps in the absence of time data .. versionadded:: 0.11.0 """ try: return self.data['dt'] except KeyError: pass try: dt = self.data['dt'] = self._reader()._get_dt() return dt except AttributeError: pass warnings.warn("Reader has no dt information, set to 1.0 ps") return 1.0 @dt.setter def dt(self, new): self.data['dt'] = new @dt.deleter def dt(self): del self.data['dt'] @property def time(self): """The time in ps of this timestep This is calculated as:: time = ts.data['time_offset'] + ts.time Or, if the trajectory doesn't provide time information:: time = ts.data['time_offset'] + ts.frame * ts.dt .. versionadded:: 0.11.0 """ offset = self.data.get('time_offset', 0) try: return self.data['time'] + offset except KeyError: return self.dt * self.frame + offset @time.setter def time(self, new): self.data['time'] = new @time.deleter def time(self): del self.data['time']
[docs]class FrameIteratorBase(object): """ Base iterable over the frames of a trajectory. A frame iterable has a length that can be accessed with the :func:`len` function, and can be indexed similarly to a full trajectory. When indexed, indices are resolved relative to the iterable and not relative to the trajectory. .. versionadded:: 0.19.0 """ def __init__(self, trajectory): self._trajectory = trajectory def __len__(self): raise NotImplementedError() @staticmethod def _avoid_bool_list(frames): if isinstance(frames, list) and frames and isinstance(frames[0], bool): return np.array(frames, dtype=bool) return frames @property def trajectory(self): return self._trajectory
[docs]class FrameIteratorSliced(FrameIteratorBase): """ Iterable over the frames of a trajectory on the basis of a slice. Parameters ---------- trajectory: ProtoReader The trajectory over which to iterate. frames: slice A slice to select the frames of interest. See Also -------- FrameIteratorBase .. versionadded:: 0.19.0 """ def __init__(self, trajectory, frames): # It would be easier to store directly a range object, as it would # store its parameters in a single place, calculate its length, and # take care of most the indexing. Though, doing so is not compatible # with python 2 where xrange (or range with six) is only an iterator. super(FrameIteratorSliced, self).__init__(trajectory) self._start, self._stop, self._step = trajectory.check_slice_indices( frames.start, frames.stop, frames.step, ) def __len__(self): start, stop, step = self.start, self.stop, self.step if (step > 0 and start < stop): # We go from a lesser number to a larger one. return int(1 + (stop - 1 - start) // step) elif (step < 0 and start > stop): # We count backward from a larger number to a lesser one. return int(1 + (start - 1 - stop) // (-step)) else: # The range is empty. return 0 def __iter__(self): for i in range(self.start, self.stop, self.step): yield self.trajectory[i] self.trajectory.rewind() def __getitem__(self, frame): if isinstance(frame, numbers.Integral): length = len(self) if not -length < frame < length: raise IndexError('Index {} is out of range of the range of length {}.' .format(frame, length)) if frame < 0: frame = len(self) + frame frame = self.start + frame * self.step return self.trajectory._read_frame_with_aux(frame) elif isinstance(frame, slice): start = self.start + (frame.start or 0) * self.step if frame.stop is None: stop = self.stop else: stop = self.start + (frame.stop or 0) * self.step step = (frame.step or 1) * self.step if step > 0: start = max(0, start) else: stop = max(0, stop) new_slice = slice(start, stop, step) return FrameIteratorSliced(self.trajectory, new_slice) else: # Indexing with a lists of bools does not behave the same in all # version of numpy. frame = self._avoid_bool_list(frame) frames = np.array(list(range(self.start, self.stop, self.step)))[frame] return FrameIteratorIndices(self.trajectory, frames) @property def start(self): return self._start @property def stop(self): return self._stop @property def step(self): return self._step
[docs]class FrameIteratorAll(FrameIteratorBase): """ Iterable over all the frames of a trajectory. Parameters ---------- trajectory: ProtoReader The trajectory over which to iterate. See Also -------- FrameIteratorBase .. versionadded:: 0.19.0 """ def __init__(self, trajectory): super(FrameIteratorAll, self).__init__(trajectory) def __len__(self): return self.trajectory.n_frames def __iter__(self): return iter(self.trajectory) def __getitem__(self, frame): return self.trajectory[frame]
[docs]class FrameIteratorIndices(FrameIteratorBase): """ Iterable over the frames of a trajectory listed in a sequence of indices. Parameters ---------- trajectory: ProtoReader The trajectory over which to iterate. frames: sequence A sequence of indices. See Also -------- FrameIteratorBase """ def __init__(self, trajectory, frames): super(FrameIteratorIndices, self).__init__(trajectory) self._frames = [] for frame in frames: if not isinstance(frame, numbers.Integral): raise TypeError("Frames indices must be integers.") frame = trajectory._apply_limits(frame) self._frames.append(frame) self._frames = tuple(self._frames) def __len__(self): return len(self.frames) def __iter__(self): for frame in self.frames: yield self.trajectory._read_frame_with_aux(frame) def __getitem__(self, frame): if isinstance(frame, numbers.Integral): frame = self.frames[frame] return self.trajectory._read_frame_with_aux(frame) else: frame = self._avoid_bool_list(frame) frames = np.array(self.frames)[frame] return FrameIteratorIndices(self.trajectory, frames) @property def frames(self): return self._frames
[docs]class IOBase(object): """Base class bundling common functionality for trajectory I/O. .. versionchanged:: 0.8 Added context manager protocol. """ #: dict with units of of *time* and *length* (and *velocity*, *force*, #: ... for formats that support it) units = {'time': None, 'length': None, 'velocity': None}
[docs] def convert_pos_from_native(self, x, inplace=True): """Conversion of coordinate array x from native units to base units. Parameters ---------- x : array_like Positions to transform inplace : bool (optional) Whether to modify the array inplace, overwriting previous data Note ---- By default, the input `x` is modified in place and also returned. In-place operations improve performance because allocating new arrays is avoided. .. versionchanged:: 0.7.5 Keyword `inplace` can be set to ``False`` so that a modified copy is returned *unless* no conversion takes place, in which case the reference to the unmodified `x` is returned. """ f = units.get_conversion_factor( 'length', self.units['length'], flags['length_unit']) if f == 1.: return x if not inplace: return f * x x *= f return x
[docs] def convert_velocities_from_native(self, v, inplace=True): """Conversion of velocities array *v* from native to base units Parameters ---------- v : array_like Velocities to transform inplace : bool (optional) Whether to modify the array inplace, overwriting previous data Note ---- By default, the input *v* is modified in place and also returned. In-place operations improve performance because allocating new arrays is avoided. .. versionadded:: 0.7.5 """ f = units.get_conversion_factor( 'speed', self.units['velocity'], flags['speed_unit']) if f == 1.: return v if not inplace: return f * v v *= f return v
[docs] def convert_forces_from_native(self, force, inplace=True): """Conversion of forces array *force* from native to base units Parameters ---------- force : array_like Forces to transform inplace : bool (optional) Whether to modify the array inplace, overwriting previous data Note ---- By default, the input *force* is modified in place and also returned. In-place operations improve performance because allocating new arrays is avoided. .. versionadded:: 0.7.7 """ f = units.get_conversion_factor( 'force', self.units['force'], flags['force_unit']) if f == 1.: return force if not inplace: return f * force force *= f return force
[docs] def convert_time_from_native(self, t, inplace=True): """Convert time *t* from native units to base units. Parameters ---------- t : array_like Time values to transform inplace : bool (optional) Whether to modify the array inplace, overwriting previous data Note ---- By default, the input `t` is modified in place and also returned (although note that scalar values `t` are passed by value in Python and hence an in-place modification has no effect on the caller.) In-place operations improve performance because allocating new arrays is avoided. .. versionchanged:: 0.7.5 Keyword `inplace` can be set to ``False`` so that a modified copy is returned *unless* no conversion takes place, in which case the reference to the unmodified `x` is returned. """ f = units.get_conversion_factor( 'time', self.units['time'], flags['time_unit']) if f == 1.: return t if not inplace: return f * t t *= f return t
[docs] def convert_pos_to_native(self, x, inplace=True): """Conversion of coordinate array `x` from base units to native units. Parameters ---------- x : array_like Positions to transform inplace : bool (optional) Whether to modify the array inplace, overwriting previous data Note ---- By default, the input `x` is modified in place and also returned. In-place operations improve performance because allocating new arrays is avoided. .. versionchanged:: 0.7.5 Keyword `inplace` can be set to ``False`` so that a modified copy is returned *unless* no conversion takes place, in which case the reference to the unmodified `x` is returned. """ f = units.get_conversion_factor( 'length', flags['length_unit'], self.units['length']) if f == 1.: return x if not inplace: return f * x x *= f return x
[docs] def convert_velocities_to_native(self, v, inplace=True): """Conversion of coordinate array `v` from base to native units Parameters ---------- v : array_like Velocities to transform inplace : bool (optional) Whether to modify the array inplace, overwriting previous data Note ---- By default, the input `v` is modified in place and also returned. In-place operations improve performance because allocating new arrays is avoided. .. versionadded:: 0.7.5 """ f = units.get_conversion_factor( 'speed', flags['speed_unit'], self.units['velocity']) if f == 1.: return v if not inplace: return f * v v *= f return v
[docs] def convert_forces_to_native(self, force, inplace=True): """Conversion of force array *force* from base to native units. Parameters ---------- force : array_like Forces to transform inplace : bool (optional) Whether to modify the array inplace, overwriting previous data Note ---- By default, the input `force` is modified in place and also returned. In-place operations improve performance because allocating new arrays is avoided. .. versionadded:: 0.7.7 """ f = units.get_conversion_factor( 'force', flags['force_unit'], self.units['force']) if f == 1.: return force if not inplace: return f * force force *= f return force
[docs] def convert_time_to_native(self, t, inplace=True): """Convert time *t* from base units to native units. Parameters ---------- t : array_like Time values to transform inplace : bool, optional Whether to modify the array inplace, overwriting previous data Note ---- By default, the input *t* is modified in place and also returned. (Also note that scalar values *t* are passed by value in Python and hence an in-place modification has no effect on the caller.) .. versionchanged:: 0.7.5 Keyword *inplace* can be set to ``False`` so that a modified copy is returned *unless* no conversion takes place, in which case the reference to the unmodified *x* is returned. """ f = units.get_conversion_factor( 'time', flags['time_unit'], self.units['time']) if f == 1.: return t if not inplace: return f * t t *= f return t
[docs] def close(self): """Close the trajectory file.""" pass
def __enter__(self): return self def __exit__(self, exc_type, exc_val, exc_tb): # see http://docs.python.org/2/library/stdtypes.html#typecontextmanager self.close() return False # do not suppress exceptions
class _Readermeta(type): # Auto register upon class creation def __init__(cls, name, bases, classdict): type.__init__(type, name, bases, classdict) try: fmt = asiterable(classdict['format']) except KeyError: pass else: for f in fmt: f = f.upper() _READERS[f] = cls
[docs]class ProtoReader(six.with_metaclass(_Readermeta, IOBase)): """Base class for Readers, without a :meth:`__del__` method. Extends :class:`IOBase` with most attributes and methods of a generic Reader, with the exception of a :meth:`__del__` method. It should be used as base for Readers that do not need :meth:`__del__`, especially since having even an empty :meth:`__del__` might lead to memory leaks. See the :ref:`Trajectory API` definition in :mod:`MDAnalysis.coordinates.__init__` for the required attributes and methods. See Also -------- :class:`ReaderBase` .. versionchanged:: 0.11.0 Frames now 0-based instead of 1-based """ #: The appropriate Timestep class, e.g. #: :class:`MDAnalysis.coordinates.xdrfile.XTC.Timestep` for XTC. _Timestep = Timestep def __init__(self): # initialise list to store added auxiliary readers in # subclasses should now call super self._auxs = {} self._transformations=[] def __len__(self): return self.n_frames
[docs] @classmethod def parse_n_atoms(cls, filename, **kwargs): """Read the coordinate file and deduce the number of atoms Returns ------- n_atoms : int the number of atoms in the coordinate file Raises ------ NotImplementedError when the number of atoms can't be deduced """ raise NotImplementedError("{} cannot deduce the number of atoms" "".format(cls.__name__))
[docs] def next(self): """Forward one step to next frame.""" try: ts = self._read_next_timestep() except (EOFError, IOError): self.rewind() raise StopIteration else: for auxname in self.aux_list: ts = self._auxs[auxname].update_ts(ts) ts = self._apply_transformations(ts) return ts
def __next__(self): """Forward one step to next frame when using the `next` builtin.""" return self.next()
[docs] def rewind(self): """Position at beginning of trajectory""" self._reopen() self.next()
@property def dt(self): """Time between two trajectory frames in picoseconds.""" return self.ts.dt @property def totaltime(self): """Total length of the trajectory The time is calculated as ``(n_frames - 1) * dt``, i.e., we assume that the first frame no time as elapsed. Thus, a trajectory with two frames will be considered to have a length of a single time step `dt` and a "trajectory" with a single frame will be reported as length 0. """ return (self.n_frames - 1) * self.dt @property def frame(self): """Frame number of the current time step. This is a simple short cut to :attr:`Timestep.frame`. """ return self.ts.frame @property def time(self): """Time of the current frame in MDAnalysis time units (typically ps). This is either read straight from the Timestep, or calculated as time = :attr:`Timestep.frame` * :attr:`Timestep.dt` """ return self.ts.time @property def trajectory(self): # Makes a reader effectively commpatible with a FrameIteratorBase return self
[docs] def Writer(self, filename, **kwargs): """A trajectory writer with the same properties as this trajectory.""" raise NotImplementedError( "Sorry, there is no Writer for this format in MDAnalysis. " "Please file an enhancement request at " "https://github.com/MDAnalysis/mdanalysis/issues")
[docs] def OtherWriter(self, filename, **kwargs): """Returns a writer appropriate for *filename*. Sets the default keywords *start*, *step* and *dt* (if available). *n_atoms* is always set from :attr:`Reader.n_atoms`. See Also -------- :meth:`Reader.Writer` and :func:`MDAnalysis.Writer` """ kwargs['n_atoms'] = self.n_atoms # essential kwargs.setdefault('start', self.frame) try: kwargs.setdefault('dt', self.dt) except KeyError: pass return core.writer(filename, **kwargs)
def _read_next_timestep(self, ts=None): # pragma: no cover # Example from DCDReader: # if ts is None: # ts = self.ts # ts.frame = self._read_next_frame(etc) # return ts raise NotImplementedError( "BUG: Override _read_next_timestep() in the trajectory reader!") def __iter__(self): """ Iterate over trajectory frames. """ self._reopen() return self def _reopen(self): """Should position Reader to just before first frame Calling next after this should return the first frame """ pass def _apply_limits(self, frame): if frame < 0: frame += len(self) if frame < 0 or frame >= len(self): raise IndexError("Index {} exceeds length of trajectory ({})." "".format(frame, len(self))) return frame def __getitem__(self, frame): """Return the Timestep corresponding to *frame*. If `frame` is a integer then the corresponding frame is returned. Negative numbers are counted from the end. If frame is a :class:`slice` then an iterator is returned that allows iteration over that part of the trajectory. Note ---- *frame* is a 0-based frame index. """ if isinstance(frame, numbers.Integral): frame = self._apply_limits(frame) return self._read_frame_with_aux(frame) elif isinstance(frame, (list, np.ndarray)): if len(frame) != 0 and isinstance(frame[0], (bool, np.bool_)): # Avoid having list of bools frame = np.asarray(frame, dtype=np.bool) # Convert bool array to int array frame = np.arange(len(self))[frame] return FrameIteratorIndices(self, frame) elif isinstance(frame, slice): start, stop, step = self.check_slice_indices( frame.start, frame.stop, frame.step) if start == 0 and stop == len(self) and step == 1: return FrameIteratorAll(self) else: return FrameIteratorSliced(self, frame) else: raise TypeError("Trajectories must be an indexed using an integer," " slice or list of indices") def _read_frame(self, frame): """Move to *frame* and fill timestep with data.""" raise TypeError("{0} does not support direct frame indexing." "".format(self.__class__.__name__)) # Example implementation in the DCDReader: # self._jump_to_frame(frame) # ts = self.ts # ts.frame = self._read_next_frame(ts._x, ts._y, ts._z, # ts._unitcell, 1) # return ts def _read_frame_with_aux(self, frame): """Move to *frame*, updating ts with trajectory, transformations and auxiliary data.""" ts = self._read_frame(frame) # pylint: disable=assignment-from-no-return for aux in self.aux_list: ts = self._auxs[aux].update_ts(ts) ts = self._apply_transformations(ts) return ts def _sliced_iter(self, start, stop, step): """Generator for slicing a trajectory. *start* *stop* and *step* are 3 integers describing the slice. Error checking is not done past this point. A :exc:`NotImplementedError` is raised if random access to frames is not implemented. """ # override with an appropriate implementation e.g. using self[i] might # be much slower than skipping steps in a next() loop try: for i in range(start, stop, step): yield self._read_frame_with_aux(i) self.rewind() except TypeError: # if _read_frame not implemented raise TypeError("{0} does not support slicing." "".format(self.__class__.__name__))
[docs] def check_slice_indices(self, start, stop, step): """Check frame indices are valid and clip to fit trajectory. The usage follows standard Python conventions for :func:`range` but see the warning below. Parameters ---------- start : int or None Starting frame index (inclusive). ``None`` corresponds to the default of 0, i.e., the initial frame. stop : int or None Last frame index (exclusive). ``None`` corresponds to the default of n_frames, i.e., it includes the last frame of the trajectory. step : int or None step size of the slice, ``None`` corresponds to the default of 1, i.e, include every frame in the range `start`, `stop`. Returns ------- start, stop, step : tuple (int, int, int) Integers representing the slice Warning ------- The returned values `start`, `stop` and `step` give the expected result when passed in :func:`range` but gives unexpected behavior when passed in a :class:`slice` when ``stop=None`` and ``step=-1`` This can be a problem for downstream processing of the output from this method. For example, slicing of trajectories is implemented by passing the values returned by :meth:`check_slice_indices` to :func:`range` :: range(start, stop, step) and using them as the indices to randomly seek to. On the other hand, in :class:`MDAnalysis.analysis.base.AnalysisBase` the values returned by :meth:`check_slice_indices` are used to splice the trajectory by creating a :class:`slice` instance :: slice(start, stop, step) This creates a discrepancy because these two lines are not equivalent:: range(10, -1, -1) # [10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0] range(10)[slice(10, -1, -1)] # [] """ slice_dict = {'start': start, 'stop': stop, 'step': step} for varname, var in slice_dict.items(): if isinstance(var, numbers.Integral): slice_dict[varname] = int(var) elif (var is None): pass else: raise TypeError("{0} is not an integer".format(varname)) start = slice_dict['start'] stop = slice_dict['stop'] step = slice_dict['step'] if step == 0: raise ValueError("Step size is zero") nframes = len(self) step = step or 1 if start is None: start = 0 if step > 0 else nframes - 1 elif start < 0: start += nframes if start < 0: start = 0 if step < 0 and start >= nframes: start = nframes - 1 if stop is None: stop = nframes if step > 0 else -1 elif stop < 0: stop += nframes if step > 0 and stop > nframes: stop = nframes return start, stop, step
def __repr__(self): return ("<{cls} {fname} with {nframes} frames of {natoms} atoms>" "".format( cls=self.__class__.__name__, fname=self.filename, nframes=self.n_frames, natoms=self.n_atoms ))
[docs] def add_auxiliary(self, auxname, auxdata, format=None, **kwargs): """Add auxiliary data to be read alongside trajectory. Auxiliary data may be any data timeseries from the trajectory additional to that read in by the trajectory reader. *auxdata* can be an :class:`~MDAnalysis.auxiliary.base.AuxReader` instance, or the data itself as e.g. a filename; in the latter case an appropriate :class:`~MDAnalysis.auxiliary.base.AuxReader` is guessed from the data/file format. An appropriate `format` may also be directly provided as a key word argument. On adding, the AuxReader is initially matched to the current timestep of the trajectory, and will be updated when the trajectory timestep changes (through a call to :meth:`next()` or jumping timesteps with ``trajectory[i]``). The representative value(s) of the auxiliary data for each timestep (as calculated by the :class:`~MDAnalysis.auxiliary.base.AuxReader`) are stored in the current timestep in the ``ts.aux`` namespace under *auxname*; e.g. to add additional pull force data stored in pull-force.xvg:: u = MDAnalysis.Universe(PDB, XTC) u.trajectory.add_auxiliary('pull', 'pull-force.xvg') The representative value for the current timestep may then be accessed as ``u.trajectory.ts.aux.pull`` or ``u.trajectory.ts.aux['pull']``. See Also -------- :meth:`remove_auxiliary` Note ---- Auxiliary data is assumed to be time-ordered, with no duplicates. See the :ref:`Auxiliary API`. """ if auxname in self.aux_list: raise ValueError("Auxiliary data with name {name} already " "exists".format(name=auxname)) if isinstance(auxdata, AuxReader): aux = auxdata aux.auxname = auxname else: aux = auxreader(auxdata, format=format, auxname=auxname, **kwargs) self._auxs[auxname] = aux self.ts = aux.update_ts(self.ts)
[docs] def remove_auxiliary(self, auxname): """Clear data and close the :class:`~MDAnalysis.auxiliary.base.AuxReader` for the auxiliary *auxname*. See Also -------- :meth:`add_auxiliary` """ aux = self._check_for_aux(auxname) aux.close() del aux delattr(self.ts.aux, auxname)
@property def aux_list(self): """ Lists the names of added auxiliary data. """ return self._auxs.keys() def _check_for_aux(self, auxname): """ Check for the existance of an auxiliary *auxname*. If present, return the AuxReader; if not, raise ValueError """ if auxname in self.aux_list: return self._auxs[auxname] else: raise ValueError("No auxiliary named {name}".format(name=auxname))
[docs] def next_as_aux(self, auxname): """ Move to the next timestep for which there is at least one step from the auxiliary *auxname* within the cutoff specified in *auxname*. This allows progression through the trajectory without encountering ``NaN`` representative values (unless these are specifically part of the auxiliary data). If the auxiliary cutoff is not set, where auxiliary steps are less frequent (``auxiliary.dt > trajectory.dt``), this allows progression at the auxiliary pace (rounded to nearest timestep); while if the auxiliary steps are more frequent, this will work the same as calling :meth:`next()`. See the :ref:`Auxiliary API`. See Also -------- :meth:`iter_as_aux` """ aux = self._check_for_aux(auxname) ts = self.ts # catch up auxiliary if it starts earlier than trajectory while aux.step_to_frame(aux.step + 1, ts) is None: next(aux) # find the next frame that'll have a representative value next_frame = aux.next_nonempty_frame(ts) if next_frame is None: # no more frames with corresponding auxiliary values; stop iteration raise StopIteration # some readers set self._frame to -1, rather than self.frame, on # _reopen; catch here or doesn't read first frame while self.frame != next_frame or getattr(self, '_frame', 0) == -1: # iterate trajectory until frame is reached ts = self.next() return ts
[docs] def iter_as_aux(self, auxname): """Iterate through timesteps for which there is at least one assigned step from the auxiliary *auxname* within the cutoff specified in *auxname*. See Also -------- :meth:`next_as_aux` :meth:`iter_auxiliary` """ aux = self._check_for_aux(auxname) self._reopen() aux._restart() while True: try: yield self.next_as_aux(auxname) except StopIteration: return
[docs] def iter_auxiliary(self, auxname, start=None, stop=None, step=None, selected=None): """ Iterate through the auxiliary *auxname* independently of the trajectory. Will iterate over the specified steps of the auxiliary (defaults to all steps). Allows to access all values in an auxiliary, including those out of the time range of the trajectory, without having to also iterate through the trajectory. After interation, the auxiliary will be repositioned at the current step. Parameters ---------- auxname : str Name of the auxiliary to iterate over. (start, stop, step) : optional Options for iterating over a slice of the auxiliary. selected : lst | ndarray, optional List of steps to iterate over. Yields ------ :class:`~MDAnalysis.auxiliary.base.AuxStep` object See Also -------- :meth:`iter_as_aux` """ aux = self._check_for_aux(auxname) if selected is not None: selection = selected else: selection = slice(start, stop, step) for i in aux[selection]: yield i aux.read_ts(self.ts)
[docs] def get_aux_attribute(self, auxname, attrname): """Get the value of *attrname* from the auxiliary *auxname* Parameters ---------- auxname : str Name of the auxiliary to get value for attrname : str Name of gettable attribute in the auxiliary reader See Also -------- :meth:`set_aux_attribute` """ aux = self._check_for_aux(auxname) return getattr(aux, attrname)
[docs] def set_aux_attribute(self, auxname, attrname, new): """ Set the value of *attrname* in the auxiliary *auxname*. Parameters ---------- auxname : str Name of the auxiliary to alter attrname : str Name of settable attribute in the auxiliary reader new New value to try set *attrname* to See Also -------- :meth:`get_aux_attribute` :meth:`rename_aux` - to change the *auxname* attribute """ aux = self._check_for_aux(auxname) if attrname == 'auxname': self.rename_aux(auxname, new) else: setattr(aux, attrname, new)
[docs] def rename_aux(self, auxname, new): """ Change the name of the auxiliary *auxname* to *new*. Provided there is not already an auxiliary named *new*, the auxiliary name will be changed in ts.aux namespace, the trajectory's list of added auxiliaries, and in the auxiliary reader itself. Parameters ---------- auxname : str Name of the auxiliary to rename new : str New name to try set Raises ------ ValueError If the name *new* is already in use by an existing auxiliary. """ aux = self._check_for_aux(auxname) if new in self.aux_list: raise ValueError("Auxiliary data with name {name} already " "exists".format(name=new)) aux.auxname = new self._auxs[new] = self._auxs.pop(auxname) setattr(self.ts.aux, new, self.ts.aux[auxname]) delattr(self.ts.aux, auxname)
[docs] def get_aux_descriptions(self, auxnames=None): """Get descriptions to allow reloading the specified auxiliaries. If no auxnames are provided, defaults to the full list of added auxiliaries. Passing the resultant description to ``add_auxiliary()`` will allow recreation of the auxiliary. e.g., to duplicate all auxiliaries into a second trajectory:: descriptions = trajectory_1.get_aux_descriptions() for aux in descriptions: trajectory_2.add_auxiliary(**aux) Returns ------- list List of dictionaries of the args/kwargs describing each auxiliary. See Also -------- :meth:`MDAnalysis.auxiliary.base.AuxReader.get_description` """ if not auxnames: auxnames = self.aux_list descriptions = [self._auxs[aux].get_description() for aux in auxnames] return descriptions
@property def transformations(self): """ Returns the list of transformations""" return self._transformations @transformations.setter def transformations(self, transformations): if not self._transformations: self._transformations = transformations else: raise ValueError("Transformations are already set")
[docs] def add_transformations(self, *transformations): """Add all transformations to be applied to the trajectory. This function take as list of transformations as an argument. These transformations are functions that will be called by the Reader and given a :class:`Timestep` object as argument, which will be transformed and returned to the Reader. The transformations can be part of the :mod:`~MDAnalysis.transformations` module, or created by the user, and are stored as a list `transformations`. This list can only be modified once, and further calls of this function will raise an exception. .. code-block:: python u = MDAnalysis.Universe(topology, coordinates) workflow = [some_transform, another_transform, this_transform] u.trajectory.add_transformations(*workflow) The transformations are applied in the order given in the list `transformations`, i.e., the first transformation is the first or innermost one to be applied to the :class:`Timestep`. The example above would be equivalent to .. code-block:: python for ts in u.trajectory: ts = this_transform(another_transform(some_transform(ts))) Parameters ---------- transform_list : list list of all the transformations that will be applied to the coordinates in the order given in the list See Also -------- :mod:`MDAnalysis.transformations` """ try: self.transformations = transformations except ValueError: raise ValueError("Can't add transformations again. Please create new Universe object") else: self.ts = self._apply_transformations(self.ts)
# call reader here to apply the newly added transformation on the # current loaded frame? def _apply_transformations(self, ts): """Applies all the transformations given by the user """ for transform in self.transformations: ts = transform(ts) return ts
[docs]class ReaderBase(ProtoReader): """Base class for trajectory readers that extends :class:`ProtoReader` with a :meth:`__del__` method. New Readers should subclass :class:`ReaderBase` and properly implement a :meth:`close` method, to ensure proper release of resources (mainly file handles). Readers that are inherently safe in this regard should subclass :class:`ProtoReader` instead. See the :ref:`Trajectory API` definition in :mod:`MDAnalysis.coordinates.__init__` for the required attributes and methods. See Also -------- :class:`ProtoReader` .. versionchanged:: 0.11.0 Most of the base Reader class definitions were offloaded to :class:`ProtoReader` so as to allow the subclassing of ReaderBases without a :meth:`__del__` method. Created init method to create common functionality, all ReaderBase subclasses must now :func:`super` through this class. Added attribute :attr:`_ts_kwargs`, which is created in init. Provides kwargs to be passed to :class:`Timestep` """ def __init__(self, filename, convert_units=None, **kwargs): super(ReaderBase, self).__init__() self.filename = filename if convert_units is None: convert_units = flags['convert_lengths'] self.convert_units = convert_units ts_kwargs = {} for att in ('dt', 'time_offset'): try: val = kwargs[att] except KeyError: pass else: ts_kwargs[att] = val self._ts_kwargs = ts_kwargs
[docs] def copy(self): """Return independent copy of this Reader. New Reader will have its own file handle and can seek/iterate independently of the original. Will also copy the current state of the Timestep held in the original Reader """ new = self.__class__(self.filename, n_atoms=self.n_atoms) if self.transformations: new.add_transformations(*self.transformations) # seek the new reader to the same frame we started with new[self.ts.frame] # then copy over the current Timestep in case it has # been modified since initial load new.ts = self.ts.copy() for auxname, auxread in self._auxs.items(): new.add_auxiliary(auxname, auxread.copy()) return new
def __del__(self): for aux in self.aux_list: self._auxs[aux].close() self.close()
class _Writermeta(type): # Auto register this format upon class creation def __init__(cls, name, bases, classdict): type.__init__(type, name, bases, classdict) try: # grab the string which describes this format # could be either 'PDB' or ['PDB', 'ENT'] for multiple formats fmt = asiterable(classdict['format']) except KeyError: # not required however pass else: # does the Writer support single and multiframe writing? single = classdict.get('singleframe', True) multi = classdict.get('multiframe', False) if single: for f in fmt: f = f.upper() _SINGLEFRAME_WRITERS[f] = cls if multi: for f in fmt: f = f.upper() _MULTIFRAME_WRITERS[f] = cls
[docs]class WriterBase(six.with_metaclass(_Writermeta, IOBase)): """Base class for trajectory writers. See Trajectory API definition in :mod:`MDAnalysis.coordinates.__init__` for the required attributes and methods. """
[docs] def convert_dimensions_to_unitcell(self, ts, inplace=True): """Read dimensions from timestep *ts* and return appropriate unitcell. The default is to return ``[A,B,C,alpha,beta,gamma]``; if this is not appropriate then this method has to be overriden. """ # override if the native trajectory format does NOT use # [A,B,C,alpha,beta,gamma] lengths, angles = ts.dimensions[:3], ts.dimensions[3:] if not inplace: lengths = lengths.copy() lengths = self.convert_pos_to_native(lengths) return np.concatenate([lengths, angles])
[docs] def write(self, obj): """Write current timestep, using the supplied `obj`. Parameters ---------- obj : :class:`~MDAnalysis.core.groups.AtomGroup` or :class:`~MDAnalysis.core.universe.Universe` or a :class:`Timestep` write coordinate information associate with `obj` Note ---- The size of the `obj` must be the same as the number of atoms provided when setting up the trajectory. """ if isinstance(obj, Timestep): ts = obj else: try: ts = obj.ts except AttributeError: try: # special case: can supply a Universe, too... ts = obj.trajectory.ts except AttributeError: raise TypeError("No Timestep found in obj argument") return self.write_next_timestep(ts)
def __del__(self): self.close() def __repr__(self): try: return "< {0!s} {1!r} for {2:d} atoms >".format(self.__class__.__name__, self.filename, self.n_atoms) except (TypeError, AttributeError): # no trajectory loaded yet or a Writer that does not need e.g. # self.n_atoms return "< {0!s} {1!r} >".format(self.__class__.__name__, self.filename)
[docs] def has_valid_coordinates(self, criteria, x): """Returns ``True`` if all values are within limit values of their formats. Due to rounding, the test is asymmetric (and *min* is supposed to be negative): min < x <= max Parameters ---------- criteria : dict dictionary containing the *max* and *min* values in native units x : numpy.ndarray ``(x, y, z)`` coordinates of atoms selected to be written out Returns ------- bool """ x = np.ravel(x) return np.all(criteria["min"] < x) and np.all(x <= criteria["max"])
# def write_next_timestep(self, ts=None)
[docs]class SingleFrameReaderBase(ProtoReader): """Base class for Readers that only have one frame. To use this base class, define the method :meth:`_read_first_frame` to read from file `self.filename`. This should populate the attribute `self.ts` with a :class:`Timestep` object. .. versionadded:: 0.10.0 .. versionchanged:: 0.11.0 Added attribute "_ts_kwargs" for subclasses Keywords "dt" and "time_offset" read into _ts_kwargs """ _err = "{0} only contains a single frame" def __init__(self, filename, convert_units=None, n_atoms=None, **kwargs): super(SingleFrameReaderBase, self).__init__() self.filename = filename if convert_units is None: convert_units = flags['convert_lengths'] self.convert_units = convert_units self.n_frames = 1 self.n_atom = n_atoms ts_kwargs = {} for att in ('dt', 'time_offset'): try: val = kwargs[att] except KeyError: pass else: ts_kwargs[att] = val self._ts_kwargs = ts_kwargs self._read_first_frame()
[docs] def copy(self): """Return independent copy of this Reader. New Reader will have its own file handle and can seek/iterate independently of the original. Will also copy the current state of the Timestep held in the original Reader """ new = self.__class__(self.filename, n_atoms=self.n_atoms) new.ts = self.ts.copy() for auxname, auxread in self._auxs.items(): new.add_auxiliary(auxname, auxread.copy()) # since the transformations have already been applied to the frame # simply copy the property new.transformations = self.transformations return new
def _read_first_frame(self): # pragma: no cover # Override this in subclasses to create and fill a Timestep pass
[docs] def rewind(self): self._read_first_frame() for auxname, auxread in self._auxs.items(): self.ts = auxread.update_ts(self.ts) super(SingleFrameReaderBase, self)._apply_transformations(self.ts)
def _reopen(self): pass
[docs] def next(self): raise StopIteration(self._err.format(self.__class__.__name__))
def __iter__(self): yield self.ts return def _read_frame(self, frame): if frame != 0: raise IndexError(self._err.format(self.__class__.__name__)) return self.ts
[docs] def close(self): # all single frame readers should use context managers to access # self.filename. Explicitly setting it to the null action in case # the IOBase.close method is ever changed from that. pass
[docs] def add_transformations(self, *transformations): """ Add all transformations to be applied to the trajectory. This function take as list of transformations as an argument. These transformations are functions that will be called by the Reader and given a :class:`Timestep` object as argument, which will be transformed and returned to the Reader. The transformations can be part of the :mod:`~MDAnalysis.transformations` module, or created by the user, and are stored as a list `transformations`. This list can only be modified once, and further calls of this function will raise an exception. .. code-block:: python u = MDAnalysis.Universe(topology, coordinates) workflow = [some_transform, another_transform, this_transform] u.trajectory.add_transformations(*workflow) Parameters ---------- transform_list : list list of all the transformations that will be applied to the coordinates See Also -------- :mod:`MDAnalysis.transformations` """ #Overrides :meth:`~MDAnalysis.coordinates.base.ProtoReader.add_transformations` #to avoid unintended behaviour where the coordinates of each frame are transformed #multiple times when iterating over the trajectory. #In this method, the trajectory is modified all at once and once only. super(SingleFrameReaderBase, self).add_transformations(*transformations) for transform in self.transformations: self.ts = transform(self.ts)
def _apply_transformations(self, ts): """ Applies the transformations to the timestep.""" # Overrides :meth:`~MDAnalysis.coordinates.base.ProtoReader.add_transformations` # to avoid applying the same transformations multiple times on each frame return ts