Source code for MDAnalysis.coordinates.TRJ

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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.
# doi: 10.25080/majora-629e541a-00e
# 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
"""AMBER trajectories --- :mod:`MDAnalysis.coordinates.TRJ`

AMBER_ can write :ref:`ASCII trajectories<ascii-trajectories>` ("traj") and
:ref:`binary trajectories<netcdf-trajectories>` ("netcdf"). MDAnalysis supports
reading of both formats and writing for the binary trajectories.

Support for AMBER is still somewhat *experimental* and feedback and
contributions are highly appreciated. Use the `Issue Tracker`_ or get in touch
on the `MDAnalysis mailinglist`_.

.. rubric:: Units

AMBER trajectories are assumed to be in the following units:

* lengths in Angstrom (Å)
* time in ps (but see below)

AMBER trajectory coordinate frames are based on a custom :class:`Timestep`

.. autoclass:: Timestep

   .. attribute:: _pos

      coordinates of the atoms as a :class:`numpy.ndarray` of shape `(n_atoms, 3)`

   .. attribute:: _velocities

      velocities of the atoms as a :class:`numpy.ndarray` of shape `(n_atoms, 3)`;
      only available if the trajectory contains velocities or if the
      *velocities* = ``True`` keyword has been supplied.

   .. attribute:: _forces

      forces of the atoms as a :class:`numpy.ndarray` of shape `(n_atoms, 3)`;
      only available if the trajectory contains forces or if the
      *forces* = ``True`` keyword has been supplied.

.. _netcdf-trajectories:

Binary NetCDF trajectories

The `AMBER netcdf`_ format make use of NetCDF_ (Network Common Data
Form) format. Such binary trajectories are recognized in MDAnalysis by
the '.ncdf' suffix and read by the :class:`NCDFReader`.

Binary trajectories can also contain velocities and forces, and can record the
exact time
step. In principle, the trajectories can be in different units than the AMBER
defaults of ångström and picoseconds but at the moment MDAnalysis only supports
those and will raise a :exc:`NotImplementedError` if anything else is detected.

.. autoclass:: NCDFReader

.. autoclass:: NCDFWriter

.. autoclass:: NCDFPicklable

.. _ascii-trajectories:

ASCII TRAJ trajectories

ASCII AMBER_ TRJ coordinate files (as defined in `AMBER TRJ format`_)
are handled by the :class:`TRJReader`. It is also possible to directly
read *bzip2* or *gzip* compressed files.

AMBER ASCII trajectories are recognised by the suffix '.trj',
'.mdcrd' or '.crdbox (possibly with an additional '.gz' or '.bz2').

.. Note::

   In the AMBER community, these trajectories are often saved with the
   suffix '.crd' but this extension conflicts with the CHARMM CRD
   format and MDAnalysis *will not correctly autodetect AMBER ".crd"
   trajectories*. Instead, explicitly provide the ``format="TRJ"``
   argument to :class:`~MDAnalysis.core.universe.Universe`::

     u = MDAnalysis.Universe("top.prmtop", "traj.crd", format="TRJ")

   In this way, the AMBER :class:`TRJReader` is used.

.. rubric:: Limitations

* Periodic boxes are only stored as box lengths A, B, C in an AMBER
  trajectory; the reader always assumes that these are orthorhombic

* The trajectory does not contain time information so we simply set
  the time step to 1 ps (or the user could provide it as kwarg *dt*)

* Trajectories with fewer than 4 atoms probably fail to be read (BUG).

* If the trajectory contains exactly *one* atom then it is always
  assumed to be non-periodic (for technical reasons).

* Velocities are currently *not supported* as ASCII trajectories.

.. autoclass:: TRJReader

.. Links

.. _AMBER:
.. _AMBER TRJ format:
..    The formats page was archived as
..    Use the archived version if the original disappears. [orbeckst]
.. _AMBER netcdf format:
..    The formats page was archived as
..    Use the archived version if the original disappears. [orbeckst]
.. _AMBER netcdf:
.. _NetCDF:
.. _Issue Tracker:
.. _MDAnalysis mailinglist:

import numpy as np
import warnings
import errno
import logging
from math import isclose

import MDAnalysis
from . import base
from ..lib import util
from ..lib.util import store_init_arguments
logger = logging.getLogger("MDAnalysis.coordinates.AMBER")

    import netCDF4
except ImportError:
    netCDF4 = None
    logger.warning("netCDF4 is not available. Writing AMBER ncdf files will be slow.")

[docs]class Timestep(base.Timestep): """AMBER trajectory Timestep. The Timestep can be initialized with `arg` being an integer (the number of atoms) and an optional keyword argument `velocities` to allocate space for both coordinates and velocities; .. versionchanged:: 0.10.0 Added ability to contain Forces """ order = 'C'
[docs]class TRJReader(base.ReaderBase): """AMBER trajectory reader. Reads the ASCII formatted `AMBER TRJ format`_. Periodic box information is auto-detected. The number of atoms in a timestep *must* be provided in the `n_atoms` keyword because it is not stored in the trajectory header and cannot be reliably autodetected. The constructor raises a :exc:`ValueError` if `n_atoms` is left at its default value of ``None``. The length of a timestep is not stored in the trajectory itself but can be set by passing the `dt` keyword argument to the constructor; it is assumed to be in ps. The default value is 1 ps. .. _AMBER TRJ format: .. versionchanged:: 0.11.0 Frames now 0-based instead of 1-based. kwarg `delta` renamed to `dt`, for uniformity with other Readers """ format = ['TRJ', 'MDCRD', 'CRDBOX'] units = {'time': 'ps', 'length': 'Angstrom'} _Timestep = Timestep @store_init_arguments def __init__(self, filename, n_atoms=None, **kwargs): super(TRJReader, self).__init__(filename, **kwargs) if n_atoms is None: raise ValueError("AMBER TRJ reader REQUIRES the n_atoms keyword") self._n_atoms = n_atoms self._n_frames = None self.trjfile = None # have _read_next_timestep() open it properly! self.ts = self._Timestep(self.n_atoms, **self._ts_kwargs) # FORMAT(10F8.3) (X(i), Y(i), Z(i), i=1,NATOM) self.default_line_parser = util.FORTRANReader("10F8.3") self.lines_per_frame = int(np.ceil(3.0 * self.n_atoms / len( self.default_line_parser))) # The last line per frame might have fewer than 10 # We determine right away what parser we need for the last # line because it will be the same for all frames. last_per_line = 3 * self.n_atoms % len(self.default_line_parser) self.last_line_parser = util.FORTRANReader("{0:d}F8.3".format( last_per_line)) # FORMAT(10F8.3) BOX(1), BOX(2), BOX(3) # is this always on a separate line?? self.box_line_parser = util.FORTRANReader("3F8.3") # Now check for box self._detect_amber_box() # open file, read first frame self._read_next_timestep() def _read_frame(self, frame): if self.trjfile is None: self.open_trajectory()[frame]) self.ts.frame = frame - 1 # gets +1'd in _read_next return self._read_next_timestep() def _read_next_timestep(self): # FORMAT(10F8.3) (X(i), Y(i), Z(i), i=1,NATOM) ts = self.ts if self.trjfile is None: self.open_trajectory() # Read coordinat frame: # coordinates = numpy.zeros(3*self.n_atoms, dtype=np.float32) _coords = [] for number, line in enumerate(self.trjfile): try: _coords.extend( except ValueError: # less than 10 entries on the line: _coords.extend( if number == self.lines_per_frame - 1: # read all atoms that are there in this frame break if _coords == []: # at the end of the stream (the loop has not been entered) raise EOFError # Read box information if self.periodic: line = next(self.trjfile) box = ts.dimensions = box + [90., 90., 90.] # assumed # probably slow ... could be optimized by storing the coordinates in # X,Y,Z lists or directly filling the array; the array/reshape is not # good because it creates an intermediate array ts._pos[:] = np.array(_coords).reshape(self.n_atoms, 3) ts.frame += 1 return ts def _detect_amber_box(self): """Detecting a box in a AMBER trajectory Rewind trajectory and check for potential box data after the first frame. Set :attr:`TRJReader.periodic` to ``True`` if box was found, ``False`` otherwise. Only run at the beginning as it *rewinds* the trajctory. - see if there's data after the atoms have been read that looks like:: FORMAT(10F8.3) BOX(1), BOX(2), BOX(3) BOX : size of periodic box - this WILL fail if we have exactly 1 atom in the trajectory because there's no way to distinguish the coordinates from the box so for 1 atom we always assume no box .. TODO:: needs a Timestep that knows about AMBER unitcells! """ if self.n_atoms == 1: # for 1 atom we cannot detect the box with the current approach self.periodic = False # see _read_next_timestep()! wmsg = "Trajectory contains a single atom: assuming periodic=False" warnings.warn(wmsg) return False self._reopen() self.periodic = False # make sure that only coordinates are read self._read_next_timestep() ts = self.ts # TODO: what do we do with 1-frame trajectories? Try..except EOFError? line = next(self.trjfile) nentries = self.default_line_parser.number_of_matches(line) if nentries == 3: self.periodic = True ts.dimensions = + [90., 90., 90.] else: self.periodic = False self.close() return self.periodic @property def n_frames(self): """Number of frames (obtained from reading the whole trajectory).""" if self._n_frames is not None: # return cached value return self._n_frames try: self._n_frames = self._read_trj_n_frames(self.filename) except IOError: return 0 else: return self._n_frames def _read_trj_n_frames(self, filename): lpf = self.lines_per_frame if self.periodic: lpf += 1 self._offsets = offsets = [] counter = 0 with util.openany(self.filename) as f: line = f.readline() # ignore first line while line: if counter % lpf == 0: offsets.append(f.tell()) line = f.readline() counter += 1 offsets.pop() # last offset is EOF return len(offsets) @property def n_atoms(self): return self._n_atoms def _reopen(self): self.close() self.open_trajectory()
[docs] def open_trajectory(self): """Open the trajectory for reading and load first frame.""" self.trjfile = util.anyopen(self.filename) self.header = self.trjfile.readline() # ignore first line if len(self.header.rstrip()) > 80: # Chimera uses this check raise OSError( "Header of AMBER formatted trajectory has more than 80 chars. " "This is probably not a AMBER trajectory.") # reset ts ts = self.ts ts.frame = -1 return self.trjfile
[docs] def close(self): """Close trj trajectory file if it was open.""" if self.trjfile is None: return self.trjfile.close() self.trjfile = None
[docs]class NCDFReader(base.ReaderBase): """Reader for `AMBER NETCDF format`_ (version 1.0). AMBER binary trajectories are automatically recognised by the file extension ".ncdf". The number of atoms (`n_atoms`) does not have to be provided as it can be read from the trajectory. The trajectory reader can randomly access frames and therefore supports direct indexing (with 0-based frame indices) and full-feature trajectory iteration, including slicing. Velocities are autodetected and read into the :attr:`Timestep._velocities` attribute. Forces are autodetected and read into the :attr:`Timestep._forces` attribute. Periodic unit cell information is detected and used to populate the :attr:`Timestep.dimensions` attribute. (If no unit cell is available in the trajectory, then :attr:`Timestep.dimensions` will return ``[0,0,0,0,0,0]``). Current limitations: * only trajectories with time in ps and lengths in Angstroem are processed The NCDF reader uses :mod:`` and therefore :mod:`scipy` must be installed. It supports the *mmap* keyword argument (when reading): ``mmap=True`` is memory efficient and directly maps the trajectory on disk to memory (using the :class:`~mmap.mmap`); ``mmap=False`` may consume large amounts of memory because it loads the whole trajectory into memory but it might be faster. The default is ``mmap=None`` and then default behavior of :class:`` prevails, i.e. ``True`` when *filename* is a file name, ``False`` when *filename* is a file-like object. .. _AMBER NETCDF format: See Also -------- :class:`NCDFWriter` .. versionadded: 0.7.6 .. versionchanged:: 0.10.0 Added ability to read Forces .. versionchanged:: 0.11.0 Frame labels now 0-based instead of 1-based. kwarg `delta` renamed to `dt`, for uniformity with other Readers. .. versionchanged:: 0.17.0 Uses :mod:`` and supports the *mmap* kwarg. .. versionchanged:: 0.20.0 Now reads scale_factors for all expected AMBER convention variables. Timestep variables now adhere standard MDAnalysis units, with lengths of angstrom, time of ps, velocity of angstrom/ps and force of kJ/(mol*Angstrom). It is noted that with 0.19.2 and earlier versions, velocities would have often been reported in values of angstrom/AKMA time units instead (Issue #2323). .. versionchanged:: 1.0.0 Support for reading `degrees` units for `cell_angles` has now been removed (Issue #2327) .. versionchanged:: 2.0.0 Now use a picklable :class:``-- :class:`NCDFPicklable`. Reading of `dt` now defaults to 1.0 ps if `dt` cannot be extracted from the first two frames of the trajectory. :meth:`Writer` now also sets `convert_units`, `velocities`, `forces` and `scale_factor` information for the :class:`NCDFWriter`. """ format = ['NCDF', 'NC'] multiframe = True version = "1.0" units = {'time': 'ps', 'length': 'Angstrom', 'velocity': 'Angstrom/ps', 'force': 'kcal/(mol*Angstrom)'} _Timestep = Timestep @store_init_arguments def __init__(self, filename, n_atoms=None, mmap=None, **kwargs): self._mmap = mmap super(NCDFReader, self).__init__(filename, **kwargs) # ensure maskandscale is off so we don't end up double scaling self.trjfile = NCDFPicklable(self.filename, mmap=self._mmap, maskandscale=False) # AMBER NetCDF files should always have a convention try: conventions = self.trjfile.Conventions if not ('AMBER' in conventions.decode('utf-8').split(',') or 'AMBER' in conventions.decode('utf-8').split()): errmsg = ("NCDF trajectory {0} does not conform to AMBER " "specifications, " " " "('AMBER' must be one of the token in attribute " "Conventions)".format(self.filename)) logger.fatal(errmsg) raise TypeError(errmsg) except AttributeError: errmsg = "NCDF trajectory {0} is missing Conventions".format( self.filename) logger.fatal(errmsg) raise ValueError(errmsg) from None # AMBER NetCDF files should also have a ConventionVersion try: ConventionVersion = self.trjfile.ConventionVersion.decode('utf-8') if not ConventionVersion == self.version: wmsg = ("NCDF trajectory format is {0!s} but the reader " "implements format {1!s}".format( ConventionVersion, self.version)) warnings.warn(wmsg) logger.warning(wmsg) except AttributeError: errmsg = "NCDF trajectory {0} is missing ConventionVersion".format( self.filename) raise ValueError(errmsg) from None # The AMBER NetCDF standard enforces 64 bit offsets if not self.trjfile.version_byte == 2: errmsg = ("NCDF trajectory {0} does not conform to AMBER " "specifications, as detailed in " " " "(NetCDF file does not use 64 bit offsets " "[version_byte = 2])".format(self.filename)) logger.fatal(errmsg) raise TypeError(errmsg) # The AMBER NetCDF standard enforces 3D coordinates try: if not self.trjfile.dimensions['spatial'] == 3: errmsg = "Incorrect spatial value for NCDF trajectory file" raise TypeError(errmsg) except KeyError: errmsg = "NCDF trajectory does not contain spatial dimension" raise ValueError(errmsg) from None # AMBER NetCDF specs require program and programVersion. Warn users # if those attributes do not exist if not (hasattr(self.trjfile, 'program') and hasattr(self.trjfile, 'programVersion')): wmsg = ("NCDF trajectory {0} may not fully adhere to AMBER " "standards as either the `program` or `programVersion` " "attributes are missing".format(self.filename)) warnings.warn(wmsg) logger.warning(wmsg) try: self.n_atoms = self.trjfile.dimensions['atom'] if n_atoms is not None and n_atoms != self.n_atoms: errmsg = ("Supplied n_atoms ({0}) != natom from ncdf ({1}). " "Note: n_atoms can be None and then the ncdf value " "is used!".format(n_atoms, self.n_atoms)) raise ValueError(errmsg) except KeyError: errmsg = ("NCDF trajectory {0} does not contain atom " "information".format(self.filename)) raise ValueError(errmsg) from None try: self.n_frames = self.trjfile.dimensions['frame'] # example trajectory when read with has # dimensions['frame'] == None (indicating a record dimension that # can grow) whereas if read with netCDF4 I get # len(dimensions['frame']) == 10: in any case, we need to get # the number of frames from somewhere such as the time variable: if self.n_frames is None: self.n_frames = self.trjfile.variables['time'].shape[0] except KeyError: errmsg = (f"NCDF trajectory {self.filename} does not contain " f"frame information") raise ValueError(errmsg) from None try: self.remarks = self.trjfile.title except AttributeError: self.remarks = "" # other metadata (*= requd): # - application AMBER # # checks for not-implemented features (other units would need to be # hacked into MDAnalysis.units) self._verify_units(self.trjfile.variables['time'].units, 'picosecond') self._verify_units(self.trjfile.variables['coordinates'].units, 'angstrom') # Check for scale_factor attributes for all data variables and # store this to multiply through later (Issue #2323) self.scale_factors = {'time': None, 'cell_lengths': None, 'cell_angles': None, 'coordinates': None, 'velocities': None, 'forces': None} for variable in self.trjfile.variables: if hasattr(self.trjfile.variables[variable], 'scale_factor'): if variable in self.scale_factors: scale_factor = self.trjfile.variables[variable].scale_factor if not isinstance(scale_factor, (float, np.floating)): raise TypeError(f"{scale_factor} is not a float") self.scale_factors[variable] = scale_factor else: errmsg = ("scale_factors for variable {0} are " "not implemented".format(variable)) raise NotImplementedError(errmsg) self.has_velocities = 'velocities' in self.trjfile.variables if self.has_velocities: self._verify_units(self.trjfile.variables['velocities'].units, 'angstrom/picosecond') self.has_forces = 'forces' in self.trjfile.variables if self.has_forces: self._verify_units(self.trjfile.variables['forces'].units, 'kilocalorie/mole/angstrom') self.periodic = 'cell_lengths' in self.trjfile.variables if self.periodic: self._verify_units(self.trjfile.variables['cell_lengths'].units, 'angstrom') # As of v1.0.0 only `degree` is accepted as a unit cell_angle_units = self.trjfile.variables['cell_angles'].units self._verify_units(cell_angle_units, 'degree') self._current_frame = 0 self.ts = self._Timestep(self.n_atoms, velocities=self.has_velocities, forces=self.has_forces, reader=self, # for dt **self._ts_kwargs) # load first data frame self._read_frame(0) @staticmethod def _verify_units(eval_unit, expected_units): if eval_unit.decode('utf-8') != expected_units: errmsg = ("NETCDFReader currently assumes that the trajectory " "was written in units of {0} instead of {1}".format( eval_unit.decode('utf-8'), expected_units)) raise NotImplementedError(errmsg)
[docs] @staticmethod def parse_n_atoms(filename, **kwargs): with, mmap=None) as f: n_atoms = f.dimensions['atom'] return n_atoms
def _get_var_and_scale(self, variable, frame): """Helper function to get variable at given frame from NETCDF file and scale if necessary. Note ---- If scale_factor is 1.0 within numerical precision then we don't apply the scaling. """ scale_factor = self.scale_factors[variable] if scale_factor is None or isclose(scale_factor, 1): return self.trjfile.variables[variable][frame] else: return self.trjfile.variables[variable][frame] * scale_factor def _read_frame(self, frame): ts = self.ts if self.trjfile is None: raise IOError("Trajectory is closed") if np.dtype(type(frame)) != np.dtype(int): # convention... for netcdf could also be a slice raise TypeError("frame must be a positive integer or zero") if frame >= self.n_frames or frame < 0: raise IndexError("frame index must be 0 <= frame < {0}".format( self.n_frames)) # note: self.trjfile.variables['coordinates'].shape == (frames, n_atoms, 3) ts._pos[:] = self._get_var_and_scale('coordinates', frame) ts.time = self._get_var_and_scale('time', frame) if self.has_velocities: ts._velocities[:] = self._get_var_and_scale('velocities', frame) if self.has_forces: ts._forces[:] = self._get_var_and_scale('forces', frame) if self.periodic: unitcell = np.zeros(6) unitcell[:3] = self._get_var_and_scale('cell_lengths', frame) unitcell[3:] = self._get_var_and_scale('cell_angles', frame) ts.dimensions = unitcell if self.convert_units: self.convert_pos_from_native(ts._pos) # in-place ! self.convert_time_from_native( ts.time) # in-place ! (hope this works...) if self.has_velocities: self.convert_velocities_from_native(ts._velocities, inplace=True) if self.has_forces: self.convert_forces_from_native(ts._forces, inplace=True) if self.periodic: # in-place ! (only lengths) self.convert_pos_from_native(ts.dimensions[:3]) ts.frame = frame # frame labels are 0-based self._current_frame = frame return ts def _reopen(self): self._current_frame = -1 def _read_next_timestep(self, ts=None): if ts is None: ts = self.ts try: return self._read_frame(self._current_frame + 1) except IndexError: raise IOError from None def _get_dt(self): """Gets dt based on the time difference between the first and second frame. If missing (i.e. an IndexError is triggered), raises an AttributeError which triggers the default 1 ps return of dt(). """ try: t1 = self.trjfile.variables['time'][1] t0 = self.trjfile.variables['time'][0] except IndexError: raise AttributeError return t1 - t0
[docs] def close(self): """Close trajectory; any further access will raise an :exc:`IOError`. .. Note:: The underlying :mod:`` module may open netcdf files with :class:`~mmap.mmap` if ``mmap=True`` was set. Hence *any* reference to an array *must* be removed before the file can be closed. """ if self.trjfile is not None: self.trjfile.close() self.trjfile = None
[docs] def Writer(self, filename, **kwargs): """Returns a NCDFWriter for `filename` with the same parameters as this NCDF. All values can be changed through keyword arguments. Parameters ---------- filename : str filename of the output NCDF trajectory n_atoms : int (optional) number of atoms remarks : str (optional) string that is stored in the title field convert_units : bool (optional) ``True``: units are converted to the AMBER base format velocities : bool (optional) Write velocities into the trajectory forces : bool (optional) Write forces into the trajectory scale_time : float (optional) Scale factor for time units scale_cell_lengths : float (optional) Scale factor for cell lengths scale_cell_angles : float (optional) Scale factor for cell angles scale_coordinates : float (optional) Scale factor for coordinates scale_velocities : float (optional) Scale factor for velocities scale_forces : float (optional) Scale factor for forces Returns ------- :class:`NCDFWriter` """ n_atoms = kwargs.pop('n_atoms', self.n_atoms) kwargs.setdefault('remarks', self.remarks) kwargs.setdefault('convert_units', self.convert_units) kwargs.setdefault('velocities', self.has_velocities) kwargs.setdefault('forces', self.has_forces) for key in self.scale_factors: kwargs.setdefault(f'scale_{key}', self.scale_factors[key]) return NCDFWriter(filename, n_atoms, **kwargs)
[docs]class NCDFWriter(base.WriterBase): """Writer for `AMBER NETCDF format`_ (version 1.0). AMBER binary trajectories are automatically recognised by the file extension ".ncdf" or ".nc". Velocities are written out if they are detected in the input :class:`Timestep`. The trajectories are always written with ångström for the lengths and picoseconds for the time (and hence Å/ps for velocities and kilocalorie/mole/Å for forces). Scale factor variables for time, velocities, cell lengths, cell angles, coordinates, velocities, or forces can be passed into the writer. If so, they will be written to the NetCDF file. In this case, the trajectory data will be written to the NetCDF file divided by the scale factor value. By default, scale factor variables are not written. The only exception is for velocities, where it is set to 20.455, replicating the default behaviour of AMBER. Unit cell information is written if available. .. _AMBER NETCDF format: Parameters ---------- filename : str name of output file n_atoms : int number of atoms in trajectory file convert_units : bool (optional) ``True``: units are converted to the AMBER base format; [``True``] velocities : bool (optional) Write velocities into the trajectory [``False``] forces : bool (optional) Write forces into the trajectory [``False``] scale_time : float (optional) Scale factor for time units [`None`] scale_cell_lengths : float (optional) Scale factor for cell lengths [``None``] scale_cell_angles : float (optional) Scale factor for cell angles [``None``] scale_coordinates : float (optional) Scale factor for coordinates [``None``] scale_velocities : float (optional) Scale factor for velocities [20.455] scale_forces : float (optional) Scale factor for forces [``None``] Note ---- MDAnalysis uses :mod:`` to access AMBER files, which are in netcdf 3 format. Although :mod:`` is very fast at reading these files, it is *very* slow when writing, and it becomes slower the longer the files are. On the other hand, the netCDF4_ package (which requires the compiled netcdf library to be installed) is fast at writing but slow at reading. Therefore, we try to use :mod:`netCDF4` for writing if available but otherwise fall back to the slower :mod:``. **AMBER users** might have a hard time getting netCDF4 to work with a conda-based installation (as discussed in `Issue #506`_) because of the way that AMBER itself handles netcdf: When the AMBER environment is loaded, the following can happen when trying to import netCDF4:: >>> import netCDF4 Traceback (most recent call last): File "<string>", line 1, in <module> File "/scratch2/miniconda/envs/py35/lib/python3.5/site-packages/netCDF4/", line 3, in <module> from ._netCDF4 import * ImportError: /scratch2/miniconda/envs/py35/lib/python3.5/site-packages/netCDF4/ undefined symbol: nc_inq_var_fletcher32 The reason for this (figured out via :program:`ldd`) is that AMBER builds its own NetCDF library that it now inserts into :envvar:`LD_LIBRARY_PATH` *without the NetCDF4 API and HDF5 bindings*. Since the conda version of :mod:`netCDF4` was built against the full NetCDF package, the one :program:`ld` tries to link to at runtime (because AMBER requires :envvar:`LD_LIBRARY_PATH`) is missing some symbols. Removing AMBER from the environment fixes the import but is not really a convenient solution for users of AMBER. At the moment there is no obvious solution if one wants to use :mod:`netCDF4` and AMBER in the same shell session. If you need the fast writing capabilities of :mod:`netCDF4` then you need to unload your AMBER environment before importing MDAnalysis. .. _netCDF4: .. _`Issue #506`: Raises ------ FutureWarning When writing. The :class:`NCDFWriter` currently does not write any `scale_factor` values for the data variables. Whilst not in breach of the AMBER NetCDF standard, this behaviour differs from that of most AMBER writers, especially for velocities which usually have a `scale_factor` of 20.455. In MDAnalysis 2.0, the :class:`NCDFWriter` will enforce `scale_factor` writing to either match user inputs (either manually defined or via the :class:`NCDFReader`) or those as written by AmberTools's :program:`sander` under default operation. See Also -------- :class:`NCDFReader` .. versionadded: 0.7.6 .. versionchanged:: 0.10.0 Added ability to write velocities and forces .. versionchanged:: 0.11.0 kwarg `delta` renamed to `dt`, for uniformity with other Readers .. versionchanged:: 0.17.0 Use fast :mod:`netCDF4` for writing but fall back to slow :mod:`` if :mod:`netCDF4` is not available. .. versionchanged:: 0.20.1 Changes the `cell_angles` unit to the AMBER NetCDF convention standard of `degree` instead of the `degrees` written in previous version of MDAnalysis (Issue #2327). .. versionchanged:: 2.0.0 ``dt``, ``start``, and ``step`` keywords were unused and are no longer set. Writing of ``scale_factor`` values has now been implemented. By default only velocities write a scale_factor of 20.455 (echoing the behaviour seen from AMBER). """ format = ['NC', 'NCDF'] multiframe = True version = "1.0" units = {'time': 'ps', 'length': 'Angstrom', 'velocity': 'Angstrom/ps', 'force': 'kcal/(mol*Angstrom)'} def __init__(self, filename, n_atoms, remarks=None, convert_units=True, velocities=False, forces=False, scale_time=None, scale_cell_lengths=None, scale_cell_angles=None, scale_coordinates=None, scale_velocities=None, scale_forces=None, **kwargs): self.filename = filename if n_atoms == 0: raise ValueError("NCDFWriter: no atoms in output trajectory") self.n_atoms = n_atoms # convert length and time to base units on the fly? self.convert_units = convert_units self.remarks = remarks or "AMBER NetCDF format (MDAnalysis.coordinates.trj.NCDFWriter)" self._first_frame = True # signals to open trajectory self.trjfile = None # open on first write with _init_netcdf() self.periodic = None # detect on first write self.has_velocities = velocities self.has_forces = forces self.scale_factors = { 'time': scale_time, 'cell_lengths': scale_cell_lengths, 'cell_angles': scale_cell_angles, 'coordinates': scale_coordinates, 'velocities': scale_velocities, 'forces': scale_forces} # NCDF standard enforces float scale_factors for value in self.scale_factors.values(): if (value is not None) and ( not isinstance(value, (float, np.floating))): errmsg = f"scale_factor {value} is not a float" raise TypeError(errmsg) self.curr_frame = 0 def _init_netcdf(self, periodic=True): """Initialize netcdf AMBER 1.0 trajectory. The trajectory is opened when the first frame is written because that is the earliest time that we can detect if the output should contain periodicity information (i.e. the unit cell dimensions). Based on Joshua Adelman's ``_ in `Issue 109`_ and uses Jason Swail's hack from `ParmEd/ParmEd#722`_ to switch between :mod:`` and :mod:`netCDF4`. .. _`Issue 109`: .. _``: .. _`ParmEd/ParmEd#722`: """ if not self._first_frame: raise IOError( errno.EIO, "Attempt to write to closed file {0}".format(self.filename)) if netCDF4: ncfile = netCDF4.Dataset(self.filename, 'w', format='NETCDF3_64BIT') else: ncfile =, mode='w', version=2, maskandscale=False) wmsg = ("Could not find netCDF4 module. Falling back to MUCH " "slower implementation for writing.") logger.warning(wmsg) warnings.warn(wmsg) # Set global attributes. setattr(ncfile, 'program', 'MDAnalysis.coordinates.TRJ.NCDFWriter') setattr(ncfile, 'programVersion', MDAnalysis.__version__) setattr(ncfile, 'Conventions', 'AMBER') setattr(ncfile, 'ConventionVersion', '1.0') setattr(ncfile, 'application', 'MDAnalysis') # Create dimensions ncfile.createDimension('frame', None) # unlimited number of steps (can append) ncfile.createDimension('atom', self.n_atoms) # number of atoms in system ncfile.createDimension('spatial', 3) # number of spatial dimensions ncfile.createDimension('cell_spatial', 3) # unitcell lengths ncfile.createDimension('cell_angular', 3) # unitcell angles ncfile.createDimension('label', 5) # needed for cell_angular # Create variables. coords = ncfile.createVariable('coordinates', 'f4', ('frame', 'atom', 'spatial')) setattr(coords, 'units', 'angstrom') if self.scale_factors['coordinates']: coords.scale_factor = self.scale_factors['coordinates'] spatial = ncfile.createVariable('spatial', 'c', ('spatial', )) spatial[:] = np.asarray(list('xyz')) time = ncfile.createVariable('time', 'f4', ('frame',)) setattr(time, 'units', 'picosecond') if self.scale_factors['time']: time.scale_factor = self.scale_factors['time'] self.periodic = periodic if self.periodic: cell_lengths = ncfile.createVariable('cell_lengths', 'f8', ('frame', 'cell_spatial')) setattr(cell_lengths, 'units', 'angstrom') if self.scale_factors['cell_lengths']: cell_lengths.scale_factor = self.scale_factors['cell_lengths'] cell_spatial = ncfile.createVariable('cell_spatial', 'c', ('cell_spatial', )) cell_spatial[:] = np.asarray(list('abc')) cell_angles = ncfile.createVariable('cell_angles', 'f8', ('frame', 'cell_angular')) setattr(cell_angles, 'units', 'degree') if self.scale_factors['cell_angles']: cell_angles.scale_factor = self.scale_factors['cell_angles'] cell_angular = ncfile.createVariable('cell_angular', 'c', ('cell_angular', 'label')) cell_angular[:] = np.asarray([list('alpha'), list('beta '), list( 'gamma')]) # These properties are optional, and are specified on Writer creation if self.has_velocities: velocs = ncfile.createVariable('velocities', 'f4', ('frame', 'atom', 'spatial')) setattr(velocs, 'units', 'angstrom/picosecond') if self.scale_factors['velocities']: velocs.scale_factor = self.scale_factors['velocities'] if self.has_forces: forces = ncfile.createVariable('forces', 'f4', ('frame', 'atom', 'spatial')) setattr(forces, 'units', 'kilocalorie/mole/angstrom') if self.scale_factors['forces']: forces.scale_factor = self.scale_factors['forces'] # Important for netCDF4! Disable maskandscale for created variables! # Won't work if called before variable creation! if netCDF4: ncfile.set_auto_maskandscale(False) ncfile.sync() self._first_frame = False self.trjfile = ncfile
[docs] def is_periodic(self, ts): """Test if timestep ``ts`` contains a periodic box. Parameters ---------- ts : :class:`Timestep` :class:`Timestep` instance containing coordinates to be written to trajectory file Returns ------- bool Return ``True`` if `ts` contains a valid simulation box """ return ts.dimensions is not None
def _write_next_frame(self, ag): """Write information associated with ``ag`` at current frame into trajectory Parameters ---------- ag : AtomGroup or Universe .. versionchanged:: 1.0.0 Added ability to use either AtomGroup or Universe. Renamed from `write_next_timestep` to `_write_next_frame`. .. versionchanged:: 2.0.0 Deprecated support for Timestep argument has now been removed. Use AtomGroup or Universe as an input instead. """ try: # Atomgroup? ts = ag.ts except AttributeError: try: # Universe? ts = ag.trajectory.ts except AttributeError: errmsg = "Input obj is neither an AtomGroup or Universe" raise TypeError(errmsg) from None if ts.n_atoms != self.n_atoms: raise IOError( "NCDFWriter: Timestep does not have the correct number of atoms") if self.trjfile is None: # first time step: analyze data and open trajectory accordingly self._init_netcdf(periodic=self.is_periodic(ts)) return self._write_next_timestep(ts) def _set_frame_var_and_scale(self, varname, data): """Helper function to set variables and scale them if necessary. Note ---- If scale_factor is numerically close to 1.0, the variable data is not scaled. """ sfactor = self.scale_factors[varname] if sfactor is None or isclose(sfactor, 1): self.trjfile.variables[varname][self.curr_frame] = data else: self.trjfile.variables[varname][self.curr_frame] = data / sfactor def _write_next_timestep(self, ts): """Write coordinates and unitcell information to NCDF file. Do not call this method directly; instead use :meth:`write` because some essential setup is done there before writing the first frame. Based on Joshua Adelman's ``_ in `Issue 109`_. .. _`Issue 109`: .. _``: .. versionchanged:: 2.0.0 Can now write scale_factors, and scale variables accordingly. """ pos = ts._pos time = ts.time unitcell = ts.dimensions if self.convert_units: # make a copy of the scaled positions so that the in-memory # timestep is not changed (would have lead to wrong results if # analysed *after* writing a time step to disk). The new # implementation could lead to memory problems and/or slow-down for # very big systems because we temporarily create a new array pos # for each frame written pos = self.convert_pos_to_native(pos, inplace=False) time = self.convert_time_to_native(time, inplace=False) unitcell = self.convert_dimensions_to_unitcell(ts) # write step # coordinates self._set_frame_var_and_scale('coordinates', pos) # time self._set_frame_var_and_scale('time', time) # unitcell if self.periodic: # cell lengths self._set_frame_var_and_scale('cell_lengths', unitcell[:3]) self._set_frame_var_and_scale('cell_angles', unitcell[3:]) # velocities if self.has_velocities: velocities = ts._velocities if self.convert_units: velocities = self.convert_velocities_to_native( velocities, inplace=False) self._set_frame_var_and_scale('velocities', velocities) # forces if self.has_forces: forces = ts._forces if self.convert_units: forces = self.convert_forces_to_native( forces, inplace=False) self._set_frame_var_and_scale('forces', forces) self.trjfile.sync() self.curr_frame += 1
[docs] def close(self): if self.trjfile is not None: self.trjfile.close() self.trjfile = None
[docs]class NCDFPicklable( """NetCDF file object (read-only) that can be pickled. This class provides a file-like object (as returned by :class:``) that, unlike standard Python file objects, can be pickled. Only read mode is supported. When the file is pickled, filename and mmap of the open file handle in the file are saved. On unpickling, the file is opened by filename, and the mmap file is loaded. This means that for a successful unpickle, the original file still has to be accessible with its filename. .. note:: This class subclasses :class:``, please see the `scipy netcdf API documentation`_ for more information on the parameters and how the class behaviour. Parameters ---------- filename : str or file-like a filename given a text or byte string. mmap : None or bool, optional Whether to mmap `filename` when reading. True when `filename` is a file name, False when `filename` is a file-like object. version : {1, 2}, optional Sets the netcdf file version to read / write. 1 is classic, 2 is 64-bit offset format. Default is 1 (but note AMBER ncdf *requires* 2). maskandscale : bool, optional Whether to automatically scale and mask data. Default is False. Example ------- :: f = NCDFPicklable(NCDF) print(f.variables['coordinates'].data) f.close() can also be used as context manager:: with NCDFPicklable(NCDF) as f: print(f.variables['coordinates'].data) See Also --------- :class:`MDAnalysis.lib.picklable_file_io.FileIOPicklable` :class:`MDAnalysis.lib.picklable_file_io.BufferIOPicklable` :class:`MDAnalysis.lib.picklable_file_io.TextIOPicklable` :class:`MDAnalysis.lib.picklable_file_io.GzipPicklable` :class:`MDAnalysis.lib.picklable_file_io.BZ2Picklable` .. versionadded:: 2.0.0 .. _`scipy netcdf API documentation`: """ def __getstate__(self): return (self.filename, self.use_mmap, self.version_byte, self.maskandscale) def __setstate__(self, args): self.__init__(args[0], mmap=args[1], version=args[2], maskandscale=args[3])