# -*- Mode: python; tab-width: 4; indent-tabs-mode:nil; coding:utf-8 -*-
# vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4
#
# 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)
#
# Released under the Lesser GNU Public Licence, v2.1 or any higher version
#
# 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.
Note
----
Support for AMBER is still somewhat *experimental* and feedback and
contributions are highly appreciated. Use the `Issue Tracker`_ or get in touch
on the `GitHub Discussions`_.
.. rubric:: Units
AMBER trajectories are assumed to be in the following units:
* lengths in Angstrom (Å)
* time in ps (but see below)
.. _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
:members:
.. autoclass:: NCDFWriter
:members:
.. autoclass:: NCDFPicklable
:members:
.. _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
boxes.
* 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
:members:
.. Links
.. _AMBER: http://ambermd.org
.. _AMBER TRJ format: http://ambermd.org/formats.html#trajectory
.. The formats page was archived as
.. http://www.webcitation.org/query?url=http%3A%2F%2Fambermd.org%2Fformats.html&date=2018-02-11
.. Use the archived version if the original disappears. [orbeckst]
.. _AMBER netcdf format: http://ambermd.org/netcdf/nctraj.xhtml
.. The formats page was archived as
.. http://www.webcitation.org/query?url=http%3A%2F%2Fambermd.org%2Fnetcdf%2Fnctraj.xhtml&date=2018-02-11
.. Use the archived version if the original disappears. [orbeckst]
.. _AMBER netcdf: http://ambermd.org/netcdf/nctraj.xhtml
.. _NetCDF: http://www.unidata.ucar.edu/software/netcdf
.. _Issue Tracker: https://github.com/MDAnalysis/mdanalysis/issues
.. _GitHub Discussions: https://github.com/MDAnalysis/mdanalysis/discussions
"""
import scipy.io.netcdf
import numpy as np
import warnings
import errno
import logging
from math import isclose
import MDAnalysis
from .timestep import Timestep
from . import base
from ..lib import util
from ..lib.util import store_init_arguments
logger = logging.getLogger("MDAnalysis.coordinates.AMBER")
try:
import netCDF4
except ImportError:
netCDF4 = None
logger.warning("netCDF4 is not available. Writing AMBER ncdf files will be slow.")
[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: http://ambermd.org/formats.html#trajectory
.. 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()
self.trjfile.seek(self._offsets[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(self.default_line_parser.read(line))
except ValueError:
# less than 10 entries on the line:
_coords.extend(self.last_line_parser.read(line))
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 = self.box_line_parser.read(line)
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 = self.box_line_parser.read(line) + [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:`scipy.io.netcdf` 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:`scipy.io.netcdf_file` prevails, i.e. ``True`` when
*filename* is a file name, ``False`` when *filename* is a file-like object.
.. _AMBER NETCDF format: http://ambermd.org/netcdf/nctraj.xhtml
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:`scipy.io.netcdf` 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:`scipy.io.netcdf_file`--
: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, "
"http://ambermd.org/netcdf/nctraj.xhtml "
"('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 "
"https://ambermd.org/netcdf/nctraj.xhtml "
"(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 scipy.io.netcdf 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['coordinates'].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)
try:
self._verify_units(self.trjfile.variables['time'].units, 'picosecond')
self.has_time = True
except KeyError:
self.has_time = False
wmsg = ("NCDF trajectory does not contain `time` information;"
" `time` will be set as an increasing index")
warnings.warn(wmsg)
logger.warning(wmsg)
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 scipy.io.netcdf_file(filename, 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)
if self.has_time:
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, KeyError):
raise AttributeError
return t1 - t0
[docs] def close(self):
"""Close trajectory; any further access will raise an :exc:`IOError`.
.. Note:: The underlying :mod:`scipy.io.netcdf` 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: http://ambermd.org/netcdf/nctraj.xhtml
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:`scipy.io.netcdf` to access AMBER files, which are in
netcdf 3 format. Although :mod:`scipy.io.netcdf` 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:`scipy.io.netcdf`.
**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/__init__.py", line 3, in <module>
from ._netCDF4 import *
ImportError: /scratch2/miniconda/envs/py35/lib/python3.5/site-packages/netCDF4/_netCDF4.cpython-35m-x86_64-linux-gnu.so: 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: https://unidata.github.io/netcdf4-python/
.. _`Issue #506`:
https://github.com/MDAnalysis/mdanalysis/issues/506#issuecomment-225081416
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:`scipy.io.netcdf` 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 `netcdf4storage.py`_ in `Issue 109`_ and uses
Jason Swail's hack from `ParmEd/ParmEd#722`_ to switch between
:mod:`scipy.io.netcdf` and :mod:`netCDF4`.
.. _`Issue 109`:
https://github.com/MDAnalysis/mdanalysis/issues/109
.. _`netcdf4storage.py`:
https://storage.googleapis.com/google-code-attachments/mdanalysis/issue-109/comment-2/netcdf4storage.py
.. _`ParmEd/ParmEd#722`: https://github.com/ParmEd/ParmEd/pull/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 = scipy.io.netcdf_file(self.filename,
mode='w', version=2,
maskandscale=False)
wmsg = ("Could not find netCDF4 module. Falling back to MUCH "
"slower scipy.io.netcdf 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 `netcdf4storage.py`_ in `Issue 109`_.
.. _`Issue 109`:
https://github.com/MDAnalysis/mdanalysis/issues/109
.. _`netcdf4storage.py`:
https://storage.googleapis.com/google-code-attachments/mdanalysis/issue-109/comment-2/netcdf4storage.py
.. 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(scipy.io.netcdf_file):
"""NetCDF file object (read-only) that can be pickled.
This class provides a file-like object (as returned by
:class:`scipy.io.netcdf_file`) 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:`scipy.io.netcdf_file`, 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`: https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.netcdf_file.html
"""
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])