6.9. H5MD trajectories — `MDAnalysis.coordinates.H5MD`¶

The H5MD trajectory file format is based upon the general, high performance HDF5 file format. HDF5 files are self documenting and can be accessed with the h5py library. HDF5 can make use of parallel file system features through the MPI-IO interface of the HDF5 library to improve parallel reads and writes.

The HDF5 library and h5py must be installed; otherwise, H5MD files cannot be read by MDAnalysis. If h5py is not installed, a RuntimeError is raised.

6.9.1. Units¶

H5MD files are very flexible and can store data in a wide range of physical units. The H5MDReader will attempt to match the units in order to convert all data to the standard MDAnalysis units (see MDAnalysis.units).

Units are read from the attributes of the position, velocity, force, and time datasets provided by the H5MD file. The unit string is translated from H5MD notation to MDAnalysis notation. If MDAnalysis does not recognize the unit (likely because that unit string is not defined in MDAnalysis.units) provided, a RuntimeError is raised. If no units are provided, MDAnalysis stores a value of None for each unit. If the H5MD file does not contain units and convert_units=True, MDAnalysis will raise a ValueError. To load a universe from an H5MD file with no units defined, set convert_units=False.

H5MDWriter detects the native units of the parent trajectory and writes the trajectory with those units, unless one of timeunit, lengthunit, velocityunit, forceunit arugments are supplied. In this case, the writer will write the corresponding dataset with the selected unit only if it is recognized by MDAnalysis units.

6.9.2. Example: Loading an H5MD simulation¶

To load an H5MD simulation from an H5MD trajectory data file (using the H5MDReader), pass the topology and trajectory files to Universe:

import MDAnalysis as mda
u = mda.Universe("topology.tpr", "trajectory.h5md")

It is also possible to pass an open h5py.File file stream into the reader:

import MDAnalysis as mda
with h5py.File("trajectory.h5md", 'r') as f:
     u = mda.Universe("topology.tpr", f)

Note

Directly using a h5py.File does not work yet. See issue #2884.

6.9.3. Example: Writing an H5MD file¶

To write to an H5MD file from a trajectory loaded with MDAnalysis, do:

import MDAnalysis as mda
u = mda.Universe("topology.tpr", "trajectory.h5md")
with mda.Writer("output.h5md", n_atoms=u.trajectory.n_atoms) as W:
    for ts in u.trajectory:
        W.write(u)

To write an H5MD file with contiguous datasets, you must specify the number of frames to be written and set chunks=False:

with mda.Writer("output_contigous.h5md", n_atoms=u.trajectory.n_atoms,
                n_frames=3, chunks=False) as W:
    for ts in u.trajectory[:3]:
        W.write(u)

The writer also supports writing directly from an AtomGroup:

ag = u.select_atoms("protein and name CA")
ag.write("alpha_carbons.h5md", frames='all')

6.9.4. Example: Opening an H5MD file in parallel¶

The parallel features of HDF5 can be accessed through h5py (see parallel h5py docs for more detail) by using the mpi4py Python package with a Parallel build of HDF5. To load a an H5MD simulation with parallel HDF5, pass driver and comm arguments to Universe:

import MDAnalysis as mda
from mpi4py import MPI
u = mda.Universe("topology.tpr", "trajectory.h5md",
                 driver="mpio", comm=MPI.COMM_WORLD)

Note

h5py must be built with parallel features enabled on top of a parallel HDF5 build, and HDF5 and mpi4py must be built with a working MPI implementation. See instructions below.

6.9.4.1. Building parallel h5py and HDF5 on Linux¶

Building a working parallel HDF5/h5py/mpi4py environment can be challenging and is often specific to your local computing resources, e.g., the supercomputer that you’re running on typically already has its preferred MPI installation. As a starting point we provide instructions that worked in a specific, fairly generic environment.

These instructions successfully built parallel HDF5/h5py with OpenMPI 4.0.4, HDF5 1.10.6, h5py 2.9.0, and mpi4py 3.0.3 on Ubuntu 16.0.6. You may have to play around with different combinations of versions of h5py/HDF5 to get a working parallel build.

Build MPI from sources
Build HDF5 from sources with parallel settings enabled:
./configure --enable-parallel --enable-shared
make
make install
Install mpi4py, making sure to point mpicc to where you’ve installed your MPI implemenation:
env MPICC=/path/to/mpicc pip install mpi4py
Build h5py from sources, making sure to enable mpi and to point to your parallel build of HDF5:
export HDF5_PATH=path-to-parallel-hdf5
python setup.py clean --all
python setup.py configure -r --hdf5-version=X.Y.Z --mpi --hdf5=$HDF5_PATH
export gcc=gcc
CC=mpicc HDF5_DIR=$HDF5_PATH python setup.py build
python setup.py install

If you have questions or want to share how you managed to build parallel hdf5/h5py/mpi4py please let everyone know on the MDAnalysis forums.

6.9.5. Classes¶

class MDAnalysis.coordinates.H5MD.H5MDReader(filename, convert_units=True, driver=None, comm=None, **kwargs)[source]¶

Reader for the H5MD format.

See h5md documentation for a detailed overview of the H5MD file format.

The reader attempts to convert units in the trajectory file to the standard MDAnalysis units (MDAnalysis.units) if convert_units is set to True.

Additional data in the observables group of the H5MD file are loaded into the Timestep.data dictionary.

Only 3D-periodic boxes or no periodicity are supported; for no periodicity, Timestep.dimensions will return None.

Although H5MD can store varying numbers of particles per time step as produced by, e.g., GCMC simulations, MDAnalysis can currently only process a fixed number of particles per step. If the number of particles changes a ValueError is raised.

The H5MDReader reads .h5md files with the following HDF5 hierarchy:

Notation:
(name) is an HDF5 group that the reader recognizes
{name} is an HDF5 group with arbitrary name
[variable] is an HDF5 dataset
<dtype> is dataset datatype
+-- is an attribute of a group or dataset

H5MD root
 \-- (h5md)
    +-- version <int>
    \-- author
        +-- name <str>, author's name
        +-- email <str>, optional email address
    \-- creator
        +-- name <str>, file that created .h5md file
        +-- version
 \-- (particles)
    \-- {group1}
        \-- (box)
            +-- dimension : <int>, number of spatial dimensions
            +-- boundary : <str>, boundary conditions of unit cell
            \-- (edges)
                \-- [step] <int>, gives frame
                \-- [value] <float>, gives box dimensions
                    +-- unit <str>
        \-- (position)
            \-- [step] <int>, gives frame
            \-- [time] <float>, gives time
                +-- unit <str>
            \-- [value] <float>, gives numpy arrary of positions
                                 with shape (n_atoms, 3)
                +-- unit <str>
        \-- (velocity)
            \-- [step] <int>, gives frame
            \-- [time] <float>, gives time
                +-- unit <str>
            \-- [value] <float>, gives numpy arrary of velocities
                                 with shape (n_atoms, 3)
                +-- unit <str>
        \-- (force)
            \-- [step] <int>, gives frame
            \-- [time] <float>, gives time
                +-- unit <str>
            \-- [value] <float>, gives numpy arrary of forces
                                 with shape (n_atoms, 3)
                +-- unit <str>
 \-- (observables)
    \-- (lambda)
        \-- [step] <int>, gives frame
        \-- [time] <float>, gives time
        \-- [value] <float>
    \-- (step)
        \-- [step] <int>, gives frame
        \-- [time] <float>, gives time
        \-- [value] <int>, gives integration step

Note

The reader does not currently read mass or charge data.

Note

If the driver and comm arguments were used to open the hdf5 file (specifically, driver="mpio") then the _reopen() method does not close and open the file like most readers because the information about the MPI communicator would be lost; instead it rewinds the trajectory back to the first timestep.

New in version 2.0.0.

Parameters:

filename (str or h5py.File) – trajectory filename or open h5py file
convert_units (bool (optional)) – convert units to MDAnalysis units
driver (str (optional)) – H5PY file driver used to open H5MD file
comm (MPI.Comm (optional)) – MPI communicator used to open H5MD file Must be passed with ‘mpio’ file driver
**kwargs (dict) – General reader arguments.

Raises:

RuntimeError – when H5PY is not installed
RuntimeError – when a unit is not recognized by MDAnalysis
ValueError – when n_atoms changes values between timesteps
ValueError – when convert_units=True but the H5MD file contains no units
ValueError – when dimension of unitcell is not 3
ValueError – when an MPI communicator object is passed to the reader but driver != 'mpio'
NoDataError – when the H5MD file has no ‘position’, ‘velocity’, or ‘force’ group

_reopen()[source]¶: reopen trajectory

Note

If the driver and comm arguments were used to open the hdf5 file (specifically, driver="mpio") then this method does not close and open the file like most readers because the information about the MPI communicator would be lost; instead it rewinds the trajectory back to the first timstep.

OtherWriter(filename, **kwargs)¶

Returns a writer appropriate for filename.

Sets the default keywords start, step and dt (if available). n_atoms is always set from Reader.n_atoms.

See also

Reader.Writer()

Writer(filename, n_atoms=None, **kwargs)[source]¶

Return writer for trajectory format

Note

The chunk shape of the input file will not be copied to the output file, as H5MDWriter uses a chunk shape of (1, n_atoms, 3) by default. To use a custom chunk shape, you must specify the chunks argument. If you would like to copy an existing chunk format from a dataset (positions, velocities, or forces), do the following:

chunks = u.trajectory._particle_group['position/value'].chunks

Note that the writer will set the same layout for all particle groups.

See also

H5MDWriter

New in version 2.0.0.

add_auxiliary(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 AuxReader instance, or the data itself as e.g. a filename; in the latter case an appropriate 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 next() or jumping timesteps with trajectory[i]).

The representative value(s) of the auxiliary data for each timestep (as calculated by the 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

remove_auxiliary()

Note

Auxiliary data is assumed to be time-ordered, with no duplicates. See the Auxiliary API.

add_transformations(*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 Timestep object as argument, which will be transformed and returned to the Reader. The transformations can be part of the 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.

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 Timestep. The example above would be equivalent to

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

MDAnalysis.transformations

aux_list¶: Lists the names of added auxiliary data.

check_slice_indices(start, stop, step)¶

Check frame indices are valid and clip to fit trajectory.

The usage follows standard Python conventions for 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 – Integers representing the slice
Return type:	tuple (int, int, int)

Warning

The returned values start, stop and step give the expected result when passed in range() but gives unexpected behavior when passed in a 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 check_slice_indices() to range()

range(start, stop, step)

and using them as the indices to randomly seek to. On the other hand, in MDAnalysis.analysis.base.AnalysisBase the values returned by check_slice_indices() are used to splice the trajectory by creating a 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)]  # []

close()[source]¶: close reader

convert_forces_from_native(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.

New in version 0.7.7.

convert_forces_to_native(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.

New in version 0.7.7.

convert_pos_from_native(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.

Changed in version 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.

convert_pos_to_native(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.

Changed in version 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.

convert_time_from_native(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.

Changed in version 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.

convert_time_to_native(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.)

Changed in version 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.

convert_velocities_from_native(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.

New in version 0.7.5.

convert_velocities_to_native(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.

New in version 0.7.5.

copy()¶

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

dt¶: Time between two trajectory frames in picoseconds.

frame¶

Frame number of the current time step.

This is a simple short cut to Timestep.frame.

get_aux_attribute(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

set_aux_attribute()

get_aux_descriptions(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 of dictionaries of the args/kwargs describing each auxiliary.
Return type:	list

has_forces¶: True if ‘force’ group is in trajectory.

has_positions¶: True if ‘position’ group is in trajectory.

has_velocities¶: True if ‘velocity’ group is in trajectory.

iter_as_aux(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

next_as_aux(), iter_auxiliary()

iter_auxiliary(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. stop, step) ((start,) – Options for iterating over a slice of the auxiliary. selected (lst \| ndarray, optional) – List of steps to iterate over.
Yields:	`AuxStep` object

See also

iter_as_aux()

n_frames¶: number of frames in trajectory

next()¶: Forward one step to next frame.

next_as_aux(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 next().

See the Auxiliary API.

See also

iter_as_aux()

open_trajectory()[source]¶: opens the trajectory file using h5py library

classmethod parse_n_atoms(filename, **kwargs)¶

Read the coordinate file and deduce the number of atoms

Returns:	n_atoms – the number of atoms in the coordinate file
Return type:	int
Raises:	`NotImplementedError` – when the number of atoms can’t be deduced

remove_auxiliary(auxname)¶: Clear data and close the AuxReader for the auxiliary auxname.

See also

add_auxiliary()

rename_aux(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.

rewind()¶: Position at beginning of trajectory

set_aux_attribute(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

get_aux_attribute(), rename_aux()

time¶

Time of the current frame in MDAnalysis time units (typically ps).

This is either read straight from the Timestep, or calculated as time = Timestep.frame * Timestep.dt

totaltime¶

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.

transformations¶: Returns the list of transformations

class MDAnalysis.coordinates.H5MD.H5MDWriter(filename, n_atoms, n_frames=None, driver=None, convert_units=True, chunks=None, compression=None, compression_opts=None, positions=True, velocities=True, forces=True, timeunit=None, lengthunit=None, velocityunit=None, forceunit=None, author='N/A', author_email=None, creator='MDAnalysis', creator_version='2.0.0', **kwargs)[source]¶

Writer for H5MD format (version 1.1).

H5MD trajectories are automatically recognised by the file extension “.h5md”.

All data from the input Timestep is written by default. For detailed information on how H5MDWriter handles units, compression, and chunking, see the Notes section below.

Note

Parellel writing with the use of a MPI communicator and the 'mpio' HDF5 driver is currently not supported.

Note

NoDataError is raised if no positions, velocities, or forces are found in the input trajectory. While the H5MD standard allows for this case, H5MDReader cannot currently read files without at least one of these three groups.

Note

Writing H5MD files with fancy trajectory slicing where the Timestep does not increase monotonically such as u.trajectory[[2,1,0]] or u.trajectory[[0,1,2,0,1,2]] raises a ValueError as this violates the rules of the step dataset in the H5MD standard.

Parameters:

filename (str or h5py.File) – trajectory filename or open h5py file
n_atoms (int) – number of atoms in trajectory
n_frames (int (optional)) – number of frames to be written in trajectory
driver (str (optional)) – H5PY file driver used to open H5MD file. See H5PY drivers for list of available drivers.
convert_units (bool (optional)) – Convert units from MDAnalysis to desired units
chunks (tuple (optional)) – Custom chunk layout to be applied to the position, velocity, and force datasets. By default, these datasets are chunked in (1, n_atoms, 3) blocks
compression (str or int (optional)) – HDF5 dataset compression setting to be applied to position, velocity, and force datasets. Allowed settings are ‘gzip’, ‘szip’, ‘lzf’. If an integer in range(10), this indicates gzip compression level. Otherwise, an integer indicates the number of a dynamically loaded compression filter.
compression_opts (int or tup (optional)) – Compression settings. This is an integer for gzip, 2-tuple for szip, etc. If specifying a dynamically loaded compression filter number, this must be a tuple of values. For gzip, 1 indicates the lowest level of compression and 9 indicates maximum compression.
positions (bool (optional)) – Write positions into the trajectory [True]
velocities (bool (optional)) – Write velocities into the trajectory [True]
forces (bool (optional)) – Write forces into the trajectory [True]
timeunit (str (optional)) – Option to convert values in the ‘time’ dataset to a custom unit, must be recognizable by MDAnalysis
lengthunit (str (optional)) – Option to convert values in the ‘position/value’ dataset to a custom unit, must be recognizable by MDAnalysis
velocityunit (str (optional)) – Option to convert values in the ‘velocity/value’ dataset to a custom unit, must be recognizable by MDAnalysis
forceunit (str (optional)) – Option to convert values in the ‘force/value’ dataset to a custom unit, must be recognizable by MDAnalysis
author (str (optional)) – Name of the author of the file
author_email (str (optional)) – Email of the author of the file
creator (str (optional)) – Software that wrote the file [MDAnalysis]
creator_version (str (optional)) – Version of software that wrote the file [MDAnalysis.__version__]

Raises:

RuntimeError – when H5PY is not installed
ValueError – when n_atoms is 0
ValueError – when chunks=False but the user did not specify n_frames
ValueError – when positions, velocities, and forces are all set to False
TypeError – when the input object is not a Universe or AtomGroup
IOError – when n_atoms of the Universe or AtomGroup being written does not match n_atoms passed as an argument to the writer
ValueError – when any of the optional timeunit, lengthunit, velocityunit, or forceunit keyword arguments are not recognized by MDAnalysis

Notes

By default, the writer will write all available data (positions, velocities, and forces) if detected in the input Timestep. In addition, the settings for compression and compression_opts will be read from the first available group of positions, velocities, or forces and used as the default value. To write a file without any one of these datsets, set positions, velocities, or forces to False.

Units

The H5MD format is very flexible with regards to units, as there is no standard defined unit for the format. For this reason, H5MDWriter does not enforce any units. The units of the written trajectory can be set explicitly with the keyword arguments lengthunit, velocityunit, and forceunit. If units are not explicitly specified, they are set to the native units of the trajectory that is the source of the coordinates. For example, if one converts a DCD trajectory, then positions are written in ångstrom and time in AKMA units. A GROMACS XTC will be written in nm and ps. The units are stored in the metadata of the H5MD file so when MDAnalysis loads the H5MD trajectory, the units will be automatically set correctly.

Compression

HDF5 natively supports various compression modes. To write the trajectory with compressed datasets, set compression='gzip', compression='lzf' , etc. See H5PY compression options for all supported modes of compression. An additional argument, compression_opts, can be used to fine tune the level of compression. For example, for GZIP compression, compression_opts can be set to 1 for minimum compression and 9 for maximum compression.

HDF5 Chunking

HDF5 datasets can be chunked, meaning the dataset can be split into equal sized pieces and stored in different, noncontiguous places on disk. If HDF5 tries to read an element from a chunked dataset, the entire dataset must be read, therefore an ill-thought-out chunking scheme can drastically effect file I/O performance. In the case of all MDAnalysis writers, in general, the number of frames being written is not known apriori by the writer, therefore the HDF5 must be extendable. However, the allocation of diskspace is defined when the dataset is created, therefore extendable HDF5 datasets must be chunked so as to allow dynamic storage on disk of any incoming data to the writer. In such cases where chunking isn’t explicity defined by the user, H5PY automatically selects a chunk shape via an algorithm that attempts to make mostly square chunks between 1 KiB - 1 MiB, however this can lead to suboptimal I/O performance. H5MDWriter uses a default chunk shape of (1, n_atoms, 3)``so as to mimic the typical access pattern of a trajectory by MDAnalysis. In our tests ([Jakupovic2021]_), this chunk shape led to a speedup on the order of 10x versus H5PY's auto-chunked shape. Users can set a custom chunk shape with the `chunks` argument. Additionaly, the datasets in a file can be written with a contiguous layout by setting ``chunks=False, however this must be accompanied by setting n_frames equal to the number of frames being written, as HDF5 must know how much space to allocate on disk when creating the dataset.

New in version 2.0.0.

H5MD_VERSION = (1, 1)¶: currently written version of the file format

close()¶: Close the trajectory file.

convert_dimensions_to_unitcell(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.

convert_forces_from_native(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.

New in version 0.7.7.

convert_forces_to_native(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.

New in version 0.7.7.

convert_pos_from_native(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.

Changed in version 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.

convert_pos_to_native(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.

Changed in version 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.

convert_time_from_native(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.

Changed in version 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.

convert_time_to_native(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.)

Changed in version 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.

convert_velocities_from_native(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.

New in version 0.7.5.

convert_velocities_to_native(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.

New in version 0.7.5.

data_blacklist = ['step', 'time', 'dt']¶: These variables are not written from Timestep.data dictionary to the observables group in the H5MD file

has_forces¶: True if writer is writing forces from Timestep.

has_positions¶: True if writer is writing positions from Timestep.

has_valid_coordinates(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:
Return type:	bool

has_velocities¶: True if writer is writing velocities from Timestep.

write(obj)¶

Write current timestep, using the supplied obj.

Parameters:	obj (`AtomGroup` or `Universe`) – 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.

Changed in version 2.0.0: Deprecated support for Timestep argument to write has now been removed. Use AtomGroup or Universe as an input instead.

class MDAnalysis.coordinates.H5MD.H5PYPicklable(name, mode='r', driver=None, libver=None, userblock_size=None, swmr=False, rdcc_nslots=None, rdcc_nbytes=None, rdcc_w0=None, track_order=None, fs_strategy=None, fs_persist=False, fs_threshold=1, **kwds)[source]¶

H5PY file object (read-only) that can be pickled.

This class provides a file-like object (as returned by h5py.File) that, unlike standard Python file objects, can be pickled. Only read mode is supported.

When the file is pickled, filename, mode, driver, and comm of h5py.File in the file are saved. On unpickling, the file is opened by filename, mode, driver. This means that for a successful unpickle, the original file still has to be accessible with its filename.

Parameters:	filename (str or file-like) – a filename given a text or byte string. driver (str (optional)) – H5PY file driver used to open H5MD file

Example

f = H5PYPicklable('filename', 'r')
print(f['particles/trajectory/position/value'][0])
f.close()

can also be used as context manager:

with H5PYPicklable('filename', 'r'):
    print(f['particles/trajectory/position/value'][0])

Note

Pickling of an h5py.File opened with driver=”mpio” and an MPI communicator is currently not supported

New in version 2.0.0.

Create a new file object.

See the h5py user guide for a detailed explanation of the options.

name: Name of the file on disk, or file-like object. Note: for files created with the ‘core’ driver, HDF5 still requires this be non-empty.
mode: r Readonly, file must exist (default) r+ Read/write, file must exist w Create file, truncate if exists w- or x Create file, fail if exists a Read/write if exists, create otherwise
driver: Name of the driver to use. Legal values are None (default, recommended), ‘core’, ‘sec2’, ‘stdio’, ‘mpio’, ‘ros3’.
libver: Library version bounds. Supported values: ‘earliest’, ‘v108’, ‘v110’, ‘v112’ and ‘latest’. The ‘v108’, ‘v110’ and ‘v112’ options can only be specified with the HDF5 1.10.2 library or later.
userblock_size: Desired size of user block. Only allowed when creating a new file (mode w, w- or x).
swmr: Open the file in SWMR read mode. Only used when mode = ‘r’.
rdcc_nbytes: Total size of the raw data chunk cache in bytes. The default size is 1024**2 (1 MB) per dataset.
rdcc_w0: The chunk preemption policy for all datasets. This must be between 0 and 1 inclusive and indicates the weighting according to which chunks which have been fully read or written are penalized when determining which chunks to flush from cache. A value of 0 means fully read or written chunks are treated no differently than other chunks (the preemption is strictly LRU) while a value of 1 means fully read or written chunks are always preempted before other chunks. If your application only reads or writes data once, this can be safely set to 1. Otherwise, this should be set lower depending on how often you re-read or re-write the same data. The default value is 0.75.
rdcc_nslots: The number of chunk slots in the raw data chunk cache for this file. Increasing this value reduces the number of cache collisions, but slightly increases the memory used. Due to the hashing strategy, this value should ideally be a prime number. As a rule of thumb, this value should be at least 10 times the number of chunks that can fit in rdcc_nbytes bytes. For maximum performance, this value should be set approximately 100 times that number of chunks. The default value is 521.
track_order: Track dataset/group/attribute creation order under root group if True. If None use global default h5.get_config().track_order.
fs_strategy: The file space handling strategy to be used. Only allowed when creating a new file (mode w, w- or x). Defined as: “fsm” FSM, Aggregators, VFD “page” Paged FSM, VFD “aggregate” Aggregators, VFD “none” VFD If None use HDF5 defaults.
fs_persist: A boolean value to indicate whether free space should be persistent or not. Only allowed when creating a new file. The default value is False.
fs_threshold: The smallest free-space section size that the free space manager will track. Only allowed when creating a new file. The default value is 1.
Additional keywords: Passed on to the selected file driver.

6.9. H5MD trajectories — MDAnalysis.coordinates.H5MD¶

6.9.1. Units¶

6.9.2. Example: Loading an H5MD simulation¶

6.9.3. Example: Writing an H5MD file¶

6.9.4. Example: Opening an H5MD file in parallel¶

6.9.4.1. Building parallel h5py and HDF5 on Linux¶

6.9.5. Classes¶

6.9. H5MD trajectories — `MDAnalysis.coordinates.H5MD`¶