Creating custom engines

The Engines are used to propagate the equations of motion or alter the state of the system in some other way. In this section we describe how additional engines can be added to PyRETIS. Here, we make a distinction between two types of engines:

  • Internal engines which directly interact with the underlying numpy.arrays representing positions, velocities and forces (see Fig. 13-left)
  • External engines which is used by PyRETIS to control the execution of external programs. In this case, the state of the particles is represented by a file containing the positions and velocities. As shown in the illustration below (Fig. 13-right) , this can be accessed using ParticlesExt.config which is a tuple containing a file name and an index. The interpretation is that the current state of the system can be found in the given file at the given frame/index. Further, the attribute ParticlesExt.vel_rev is used to determine if the velocities in the configuration should be reversed or not. In this case the engine will typically make use of ParticlesExt.set_pos() in order to update the state of the system.
Comparison of internal and external engines.

Fig. 13 Illustration of how internal engines (left) and external engines (right) interact with the system and particles. (Left) The internal engine interacts with and alters the state of the system, and it can directly access the positions and velocities, as numpy.arrays, using Particles.pos and Particles.vel. (Right) The external engine interacts with and alters the state of the system using ParticlesExt.set_pos() and the state of the system is represented by the attribute ParticlesExt.config.

Creating a new internal engine

In order to create a new internal engine, we create a new class for our engine which is sub-classing the generic EngineBase. Often, we wish to create a new integrator for molecular dynamics and then we can simplify our work by sub-classing the more specific MDEngine. The steps you need to complete in order to create a new MDengine is as follows:

  1. Create a new Python class which is a sub-class of MDEngine
  2. Write a method to initialise this class, that is a __init__ method. This method will be called when PyRETIS is setting up a new simulation and it will be fed variables from the PyRETIS input file.
  3. Write a method to perform a single integration step.
  4. Modify the input script to use the new integrator.

Example: Creating a new internal LeapFrog integrator

To be specific, we will in the following show the steps needed to create a new MD engine using the Leapfrog scheme [1]:

\mathbf{r} (t + \Delta t) &= \mathbf{r} (t) + \mathbf{v} (t) \Delta t + \tfrac{1}{2}
\mathbf{a} (t) \Delta t^2 \\
\mathbf{v} (t + \Delta t) &= \mathbf{v} (t) + \tfrac{1}{2} \left[\mathbf{a} (t) +
\mathbf{a} (t+\Delta t) \right] \Delta t

where \mathbf{r} are the positions, \mathbf{v} the velocities, \mathbf{a} the accelerations, \Delta t the time step and t is the current time.

Step 1 and 2: Sub-classing MEngine

In order to define a new class to use with PyRETIS we sub-class the MDEngine and define the __init__ method:

from pyretis.engines import MDEngine

class LeapFrog(MDEngine):
    """Perform Leap Frog integration."""

    def __init__(self, timestep):
        """Initiate the engine.

        timestep : float
            Set the time step for the integrator.
        super().__init__(timestep, 'Leap Frog integrator', dynamics='NVE')
        self.half_delta_t = self.delta_t * 0.5
        self.half_delta_t_sq = 0.5 * self.delta_t**2

Step 3: Adding the integration step method

Now, we add the actual integration routine:

    def integration_step(self, system):
        """Perform one step for the Leap Frog integrator."""
        particles = system.particles
        imass = particles.imass

        # get current acceleration:
        acc_t = particles.force * imass
        # update positions:
        particles.pos += (self.delta_t * particles.vel +
                          self.half_delta_t_sq * acc_t)
        # update forces:
        # update acceleration:
        acc_t2 = particles.force * imass
        # update velocities:
        particles.vel += self.half_delta_t * (acc_t + acc_t2)

Step 4: Making use of the new integrator

The new integrator can be used by specifying the following engine section:

Example Engine section for LeapFrog:
class = LeapFrog
module =
timestep = 0.002

where the keyword module specifies the file where you have stored the new LeapFrog class.

Creating a new external engine

The external engines are used to interface external programs. In PyRETIS there is a generic ExternalMDEngine which you can make use of in order to set up new custom external engines. Before we give an overview of the methods you will have to implement in order to create a new engine, we list some useful methods which are already present in the generic ExternalMDEngine:

In order to create a new external engine, there are several methods that you need to implement. These methods are described in the next section. Before reading this description, please have a look at the figure below which illustrates the interaction between PyRETIS and the external engine:

Implementing methods for a new external engine

The following methods must be implemented in the new engine:

  • __init__ which does some set-up for the external engine.
  • step which is a method for performing a single MD step with the external engine.
  • _read_configuration For reading output (configurations) from the external engine. This is used for calculating the order parameter(s).
  • _reverse_velocities For reversing velocities in a snapshot. This method will typically make use of the _read_configuration.
  • _extract_frame For extracting a single frame from a trajectory.
  • _propagate_from The method for propagating the equations of motion using the external engine.
  • modify_velocities The method used for generating random velocities for shooting points.

Some examples/templates are shown in the following sections.

The __init__ method

The __init__ method sets up the engine and makes it ready for use. This can for instance involve checking that the required input files are in place and checking the consistency of the input files.

import logging
import os
from pyretis.engines.external import ExternalMDEngine

logger = logging.getLogger(__name__)  # pylint: disable=C0103

class NewEngine(ExternalMDEngine):
    """A new external engine class

    exe : string
        The command for executing the engine.
    input_path : string
        The directory where the input files are stored.
    timestep : float
        The time step used in the external MD simulation.
    subcycles : integer
        The number of steps each external step is composed of.

    def __init__(self, exe, input_path, timestep, subcycles):
        """Initiate the script.

        exe : string
            The engine executable.
        input_path : string
            The absolute path to where the input files are stored.
        timestep : float
            The time step used in the external engine.
        subcycles : integer
            The number of steps each external step is composed of.
        super().__init__('New external engine', timestep, subcycles, '')
        # Store variables for the engine:
        self.exe = exe
        # store input path:
        self.input_path = os.path.abspath(input_path)
        # store input files:
        self.input_files = {}
        for key in REQUIRED_FILES:
            self.input_files[key] = os.path.join(self.input_path, key)
            if not os.path.isfile(self.input_files[key]):
                msg = 'Engine could not find file "{}"!'.format(key)
                raise ValueError(msg)

        # Create a pyretis version of the input file:
        settings = {'timestep': self.timestep,
                    'subcycles': self.subcycles}

        self.input_files['input'] = os.path.join(self.input_path,
                           self.input_files['input'], settings, delim='=')

In addition, this is a good point to add a method to execute the external program. Here we will make use of ExternalMDEngine.execute_command() and the ExternalMDEngine.exe_dir property. This property is set internally by PyRETIS and points to a directory which we will use when executing the external engine. As a concrete example, if we are generating a path for ensemble [1^+], this will point to the sub-directory generate of the ensemble-directory 002. Here is a short template for executing external commands:

    def _run_program(self):
        """Method to execute the new engine.

        out : dict
            The files created by the run.
        self.execute_command(self.exe, cwd=self.exe_dir, inputs=None)
        out = {}
        for key in OUTPUT_FILES:
            filename = os.path.join(self.exe_dir, key)
            if os.path.isfile(filename):
                out[key] = filename
        return out

The step method

The step method is used to execute a single step with the external software. This method is used by the PyRETIS engine in the following way:

conf = self.step(system, 'output-file')

That is the method take in two parameters: a system object and a string and return a single parameter. This single parameter is just a string which contains the output configuration created after executing the external program. Here is a short template for the step method:

    def step(self, system, name):
        """Perform a single step with the external engine.

        system : object like :py:class:`.System`
            The system we are integrating.
        name : string
            To name the output files from the step.

        out_files : dict
            The output files created by this step.
        # Get initial config:
        input_file = self.dump_frame(system)
        # copy input files, maybe rename input_file etc.
        # Then, run it
        out_files = self._run_program()
        # Store the frame after the step, make use of the name variable:
        config = os.path.join(self.exe_dir, 'OUTPUT.{}'.format(name))
        # Get energies
        ekin, vpot = get_energy(oszicar)
        # remove files that we dont need after the step:
        self._remove_files(self.exe_dir, files_to_remove)
        # Update the state of the system to match the current config
        phase_point = {'pos': (config, None),
                       'vel': False, 'vpot': vpot, 'ekin': ekin}
        return config

The _read_configuration method

The _read_configuration method is used to get the actual positions and velocities from a configuration file. As a short template:

    def _read_configuration(filename):
        """Method to read config files for the external engine.

        This method is used when we calculate the order parameter.

        filename : string
            The file to read the configuration from.

        xyz : numpy.array
            The positions.
        vel : numpy.array
            The velocities.
        xyz, vel = method_to_read_config_file(filename)
        return xyz, vel

The _reverse_velocities method

The _reverse_velocities method is used to reverse velocities for a single configuration.

    def _reverse_velocities(filename, outfile):
        """Method to reverse velocity in a given snapshot.

        filename : string
            The configuration to reverse velocities in.
        outfile : string
            The output file for storing the configuration with
            reversed velocities.
        xyz, vel = method_to_read_config_file(filename)
        method_to_write_config_file(outfile, xyz, -vel)

The _extract_frame method

The _extract_frame method is used to extract a single snapshot from a trajectory file.

    def _extract_frame(self, traj_file, idx, out_file):
        """Extract a frame from a trajectory file.

        This method is used by `self.dump_config` when we are
        dumping from a trajectory file. It is not used if we are
        dumping from a single config file. Here you can write
        your own code for dumping or make use of existing libraries
        such as mdtraj, mdanalysis, ase, ...

        traj_file : string
            The trajectory file to dump from.
        idx : integer
            The frame number we look for.
        out_file : string
            The file to dump to.

The _propagate_from method

The _propagate_from method is used to propagate the equations of motion using the external engine. In some cases this can make use of the step method, in other cases this is really distinct (e.g. in GROMACS this would correspond to extending a simulation).

    def _propagate_from(self, name, path, system, order_function, interfaces,
        """Propagate from the current system configuration.

        Here, we assume that this method is called after the propagate()
        has been called in the parent. The parent is then responsible
        for reversing the velocities and also for setting the initial
        state of the system.

        name : string
            A name to use for the trajectory we are generating.
        path : object like :py:class:`.PathBase`
            This is the path we use to fill in phase-space points.
            We are here not returning a new path - this since we want
            to delegate the creation of the path to the method
            that is running `propagate`.
        system : object like :py:class:`.System`
            The system object gives the initial state for the
            integration. The initial state is stored and the system is
            reset to the initial state when the integration is done.
        order_function : object like :py:class:`.OrderParameter`
            The object used for calculating the order parameter.
        interfaces : list of floats
            These interfaces define the stopping criterion.
        reverse : boolean
            If True, the system will be propagated backwards in time.

        success : boolean
            This is True if we generated an acceptable path.
        status : string
            A text description of the current status of the propagation.
        # Dumping of the initial config were done by the parent, here
        # we will just the dumped file:
        initial_conf = system.particles.get_pos()[0]
        # Copy this file, in case the external program requires
        # specific names for the input configurations:
        INPUT_CONF = os.path.join(self.exe_dir, 'INPUT_NAME')
        self._copyfile(initial_conf, poscar)
        # Set the positions to be just the file we copied:
        system.particles.set_pos((INPUT_CONF, None))
        # Create the output trajectory file:
        traj_file = os.path.join(self.exe_dir, name)
        # Add other files here as well, input settings, force field etc...
        success = False
        left, _, right = interfaces
        phase_point = system.particles.get_particle_state()
        # Just to be keep the same order as in the loop
        vpot = phase_point['vpot']
        ekin = phase_point['ekin']
        order = self.calculate_order(order_function, system)
        out_files = {}
        for i in range(path.maxlen):
            # We first add the current phase point, and then we propagate.
            phase_point = {
                'order': order,
                'pos': (traj_file, i),
                'vel': reverse,
                'vpot': vpot,
                'ekin': ekin}
            status, success, stop = self.add_to_path(path, phase_point,
                                                     left, right)
            # Add the current frame to the output trajectory also:
            # e.g.: add_frame_to_traj('CURRENT_FRAME_FILE', traj_file, i)
            if stop:
                logger.debug('Ending propagate at %i. Reason: %s', i, status)
            # Here execute the external program
            # for instance by using self.step() or self._run_program()
            # get energies after execution:
            ekin, vpot = get_energy
            # Recalculate order parameter
            # If the output file is named different, update the
            # system object so that it points to this file, e.g.:
            # system.particles.set_pos((NAME_OF_FILE, None)), or
            # if the energies are needed as well, use
            # system.particles.set_particle_state(...)
            order = self.calculate_order(order_function, system)
        # We are done with the execution:
        # clean up and remove files:
        self._remove_files(self.exe_dir, list_of_files_to_remove)
        return success, status

The modify_velocities method

This method is used to draw random velocities, typically when performing the shooting move. The generic modify_velocities method is quite involved, but for most cases is sufficient to only implement aimless shooting. Here is a template:

    def modify_velocities(self, system, rgen, sigma_v=None, aimless=True,
                          momentum=False, rescale=None):
        """Modify the velocities of the current state.

        This method will modify the velocities of a time slice.

        system : object like :py:class:`.System`
            System is used here since we need access to the particle
        rgen : object like :py:class:`.RandomGenerator`
            This is the random generator that will be used.
        sigma_v : numpy.array, optional
            These values can be used to set a standard deviation (one
            for each particle) for the generated velocities.
        aimless : boolean, optional
            Determines if we should do aimless shooting or not.
        momentum : boolean, optional
            If True, we reset the linear momentum to zero after generating.
        rescale : float, optional
            In some NVE simulations, we may wish to rescale the energy to
            a fixed value. If `rescale` is a float > 0, we will rescale
            the energy (after modification of the velocities) to match the
            given float.

        dek : float
            The change in the kinetic energy.
        kin_new : float
            The new kinetic energy.
        dek = None
        kin_old = None
        kin_new = None
        if rescale is not None and rescale is not False and rescale > 0:
            # Code to support energy rescale, or just ignore this as
            # set:
            msgtxt = 'External engine does not support energy rescale!'
            raise NotImplementedError(msgtxt)
            kin_old = system.particles.ekin
        if aimless:
            pos = self.dump_frame(system)
            phase_point = system.particles.get_particle_state()
            # Generate new velocities, either by hand or by asking the
            # external program.
            genvel = os.path.join(self.exe_dir, 'g_POSCAR')
            # Also get the new kinetic energy
            kin_new = kinetic_energy_output_from_velocity_generation()
            # Update the phase point (note we keep the potential energy):
            phase_point['pos'] = (genvel, None)
            phase_point['vel'] = False
            phase_point['ekin'] = kin_new
        else:  # soft velocity change, add from Gaussian dist
            # You might not need to support this:
            msgtxt = 'External engine only support aimless shooting!'
            raise NotImplementedError(msgtxt)
        if kin_old is None or kin_new is None:
            dek = float('inf')
            logger.warning(('Kinetic energy not found for previous point.'
                            '\n(This happens when the initial configuration '
                            'does not contain energies.)'))
            dek = kin_new - kin_old
        return dek, kin_new