Esempio n. 1
0
   def bind(self):
      """Calls the bind routines for all the objects in the simulation."""

      # binds important computation engines
      self.nm.bind(self.beads, self.ensemble)
      self.forces.bind(self.beads, self.cell, self.flist)
      self.ensemble.bind(self.beads, self.nm, self.cell, self.forces, self.prng)
      self.init.init_stage2(self)

      # binds output management objects
      self.properties.bind(self)
      self.trajs.bind(self)
      for o in self.outputs:
         o.bind(self)

      self.chk = CheckpointOutput("RESTART", 1, True, 0)
      self.chk.bind(self)

      # registers the softexit routine
      softexit.register(self.softexit)
Esempio n. 2
0
   def bind(self):
      """Calls the bind routines for all the objects in the simulation."""

      # binds important computation engines
      self.nm.bind(self.beads, self.ensemble)
      self.forces.bind(self.beads, self.cell, self.flist)
      self.ensemble.bind(self.beads, self.nm, self.cell, self.forces, self.prng)
      self.init.init_stage2(self)

      # binds output management objects
      self.properties.bind(self)
      self.trajs.bind(self)
      for o in self.outputs:
         o.bind(self)

      self.chk = CheckpointOutput("RESTART", 1, True, 0)
      self.chk.bind(self)

      # registers the softexit routine
      softexit.register(self.softexit)
Esempio n. 3
0
class Simulation(dobject):
   """Main simulation object.

   Contains all the references and the main dynamics loop. Also handles the
   initialization and output.

   Attributes:
      beads: A beads object giving the atom positions.
      cell: A cell object giving the system box.
      prng: A random number generator object.
      flist: A list of forcefield objects giving different ways to partially
         calculate the forces.
      forces: A Forces object for calculating the total force for all the
         replicas.
      ensemble: An ensemble object giving the objects necessary for producing
         the correct ensemble.
      tsteps: The total number of steps.
      ttime: The wall clock time (in seconds).
      format: A string specifying both the format and the extension of traj
         output.
      outputs: A list of output objects that should be printed during the run
      nm:  A helper object dealing with normal modes transformation
      properties: A property object for dealing with property output.
      trajs: A trajectory object for dealing with trajectory output.
      chk: A checkpoint object for dealing with checkpoint output.
      rollback: If set to true, the state of the simulation at the start
         of the step will be output to a restart file rather than
         the current state of the simulation. This is because we cannot
         restart from half way through a step, only from the beginning of a
         step, so this is necessary for the trajectory to be continuous.

   Depend objects:
      step: The current simulation step.
   """

   def __init__(self, beads, cell, forces, ensemble, prng, outputs, nm, init, step=0, tsteps=1000, ttime=0):
      """Initializes Simulation class.

      Args:
         beads: A beads object giving the atom positions.
         cell: A cell object giving the system box.
         forces: A forcefield object giving the force calculator for each
            replica of the system.
         ensemble: An ensemble object giving the objects necessary for
            producing the correct ensemble.
         prng: A random number object.
         outputs: A list of output objects.
         nm: A class dealing with path NM operations.
         init: A class to deal with initializing the simulation object.
         step: An optional integer giving the current simulation time step.
            Defaults to 0.
         tsteps: An optional integer giving the total number of steps. Defaults
            to 1000.
         ttime: The simulation running time. Used on restart, to keep a
            cumulative total.
      """

      info(" # Initializing simulation object ", verbosity.low )
      self.prng = prng
      self.ensemble = ensemble
      self.beads = beads
      self.cell = cell
      self.nm = nm

      # initialize the configuration of the system
      self.init = init
      init.init_stage1(self)

      self.flist = forces
      self.forces = Forces()
      self.outputs = outputs

      dset(self, "step", depend_value(name="step", value=step))
      self.tsteps = tsteps
      self.ttime = ttime

      self.properties = Properties()
      self.trajs = Trajectories()
      self.chk = None
      self.rollback = True

   def bind(self):
      """Calls the bind routines for all the objects in the simulation."""

      # binds important computation engines
      self.nm.bind(self.beads, self.ensemble)
      self.forces.bind(self.beads, self.cell, self.flist)
      self.ensemble.bind(self.beads, self.nm, self.cell, self.forces, self.prng)
      self.init.init_stage2(self)

      # binds output management objects
      self.properties.bind(self)
      self.trajs.bind(self)
      for o in self.outputs:
         o.bind(self)

      self.chk = CheckpointOutput("RESTART", 1, True, 0)
      self.chk.bind(self)

      # registers the softexit routine
      softexit.register(self.softexit)

   def softexit(self):
      """Deals with a soft exit request.

      Tries to ensure that a consistent restart checkpoint is
      written out.
      """

      if self.step < self.tsteps:
         self.step += 1
      if not self.rollback:
         self.chk.store()
      self.chk.write(store=False)

      self.forces.stop()

   def run(self):
      """Runs the simulation.

      Does all the simulation steps, and outputs data to the appropriate files
      when necessary. Also deals with starting and cleaning up the threads used
      in the communication between the driver and the PIMD code.
      """

      self.forces.run()

      # prints initial configuration -- only if we are not restarting
      if (self.step == 0):
         self.step = -1
         for o in self.outputs:
            o.write()
         self.step = 0

      steptime = 0.0
      simtime =  time.time()

      cstep = 0
      tptime = 0.0
      tqtime = 0.0
      tttime = 0.0
      ttot = 0.0
      # main MD loop
      for self.step in range(self.step,self.tsteps):
         # stores the state before doing a step.
         # this is a bit time-consuming but makes sure that we can honor soft
         # exit requests without screwing the trajectory

         steptime = -time.time()
         self.chk.store()

         self.ensemble.step()

         for o in self.outputs:
            o.write()

         if os.path.exists("EXIT"): # soft-exit
            self.rollback = False
            softexit.trigger()

         steptime += time.time()
         ttot += steptime
         tptime += self.ensemble.ptime
         tqtime += self.ensemble.qtime
         tttime += self.ensemble.ttime
         cstep += 1

         if verbosity.high or (verbosity.medium and self.step%100 == 0) or (verbosity.low and self.step%1000 == 0):
            info(" # Average timings at MD step % 7d. t/step: %10.5e [p: %10.5e  q: %10.5e  t: %10.5e]" %
               ( self.step, ttot/cstep, tptime/cstep, tqtime/cstep, tttime/cstep ) )
            cstep = 0
            tptime = 0.0
            tqtime = 0.0
            tttime = 0.0
            ttot = 0.0
            info(" # MD diagnostics: V: %10.5e    Kcv: %10.5e   Ecns: %10.5e" %
               (self.properties["potential"], self.properties["kinetic_cv"], self.properties["conserved"] ) )

         if (self.ttime > 0 and time.time() - simtime > self.ttime):
            info(" # Wall clock time expired! Bye bye!", verbosity.low )
            break

      info(" # Simulation ran successfully for the prescribed total_step! Bye bye!", verbosity.low )
      self.rollback = False
      softexit.trigger()
Esempio n. 4
0
class Simulation(dobject):
   """Main simulation object.

   Contains all the references and the main dynamics loop. Also handles the
   initialisation and output.

   Attributes:
      beads: A beads object giving the atom positions.
      cell: A cell object giving the system box.
      prng: A random number generator object.
      flist: A list of forcefield objects giving different ways to partially
         calculate the forces.
      forces: A Forces object for calculating the total force for all the
         replicas.
      ensemble: An ensemble object giving the objects necessary for producing
         the correct ensemble.
      tsteps: The total number of steps.
      ttime: The wall clock time (in seconds).
      format: A string specifying both the format and the extension of traj
         output.
      outputs: A list of output objects that should be printed during the run
      nm:  A helper object dealing with normal modes transformation
      properties: A property object for dealing with property output.
      trajs: A trajectory object for dealing with trajectory output.
      chk: A checkpoint object for dealing with checkpoint output.
      rollback: If set to true, the state of the simulation at the start
         of the step will be output to a restart file rather than
         the current state of the simulation. This is because we cannot
         restart from half way through a step, only from the beginning of a
         step, so this is necessary for the trajectory to be continuous.

   Depend objects:
      step: The current simulation step.
   """

   def __init__(self, beads, cell, forces, ensemble, prng, outputs, nm, init, step=0, tsteps=1000, ttime=0):
      """Initialises Simulation class.

      Args:
         beads: A beads object giving the atom positions.
         cell: A cell object giving the system box.
         forces: A forcefield object giving the force calculator for each
            replica of the system.
         ensemble: An ensemble object giving the objects necessary for
            producing the correct ensemble.
         prng: A random number object.
         outputs: A list of output objects.
         nm: A class dealing with path NM operations.
         init: A class to deal with initializing the simulation object.
         step: An optional integer giving the current simulation time step.
            Defaults to 0.
         tsteps: An optional integer giving the total number of steps. Defaults
            to 1000.
         ttime: The simulation running time. Used on restart, to keep a
            cumulative total.
      """

      info(" # Initializing simulation object ", verbosity.low )
      self.prng = prng
      self.ensemble = ensemble
      self.beads = beads
      self.cell = cell
      self.nm = nm

      # initialize the configuration of the system
      self.init = init
      init.init_stage1(self)

      self.flist = forces
      self.forces = Forces()
      self.outputs = outputs

      dset(self, "step", depend_value(name="step", value=step))
      self.tsteps = tsteps
      self.ttime = ttime

      self.properties = Properties()
      self.trajs = Trajectories()
      self.chk = None
      self.rollback = True

   def bind(self):
      """Calls the bind routines for all the objects in the simulation."""

      # binds important computation engines
      self.nm.bind(self.beads, self.ensemble)
      self.forces.bind(self.beads, self.cell, self.flist)
      self.ensemble.bind(self.beads, self.nm, self.cell, self.forces, self.prng)
      self.init.init_stage2(self)

      # binds output management objects
      self.properties.bind(self)
      self.trajs.bind(self)
      for o in self.outputs:
         o.bind(self)

      self.chk = CheckpointOutput("RESTART", 1, True, 0)
      self.chk.bind(self)

      # registers the softexit routine
      softexit.register(self.softexit)

   def softexit(self):
      """Deals with a soft exit request.

      Tries to ensure that a consistent restart checkpoint is
      written out.
      """

      if self.step < self.tsteps:
         self.step += 1
      if not self.rollback:
         self.chk.store()
      self.chk.write(store=False)

      self.forces.stop()

   def run(self):
      """Runs the simulation.

      Does all the simulation steps, and outputs data to the appropriate files
      when necessary. Also deals with starting and cleaning up the threads used
      in the communication between the driver and the PIMD code.
      """

      self.forces.run()

      # prints inital configuration -- only if we are not restarting
      if (self.step == 0):
         self.step = -1
         for o in self.outputs:
            o.write()
         self.step = 0

      steptime = 0.0
      simtime =  time.time()

      cstep = 0
      tptime = 0.0
      tqtime = 0.0
      tttime = 0.0
      ttot = 0.0
      # main MD loop
      for self.step in range(self.step,self.tsteps):
         # stores the state before doing a step.
         # this is a bit time-consuming but makes sure that we can honor soft
         # exit requests without screwing the trajectory

         steptime = -time.time()
         self.chk.store()

         self.ensemble.step()

         for o in self.outputs:
            o.write()

         if os.path.exists("EXIT"): # soft-exit
            self.rollback = False
            softexit.trigger()

         steptime += time.time()
         ttot += steptime
         tptime += self.ensemble.ptime
         tqtime += self.ensemble.qtime
         tttime += self.ensemble.ttime
         cstep += 1

         if verbosity.high or (verbosity.medium and self.step%100 == 0) or (verbosity.low and self.step%1000 == 0):
            info(" # Average timings at MD step % 7d. t/step: %10.5e [p: %10.5e  q: %10.5e  t: %10.5e]" %
               ( self.step, ttot/cstep, tptime/cstep, tqtime/cstep, tttime/cstep ) )
            cstep = 0
            tptime = 0.0
            tqtime = 0.0
            tttime = 0.0
            ttot = 0.0
            info(" # MD diagnostics: V: %10.5e    Kcv: %10.5e   Ecns: %10.5e" %
               (self.properties["potential"], self.properties["kinetic_cv"], self.properties["conserved"] ) )

         if (self.ttime > 0 and time.time() - simtime > self.ttime):
            info(" # Wall clock time expired! Bye bye!", verbosity.low )
            break

      info(" # Simulation ran successfully for the prescribed total_step! Bye bye!", verbosity.low )
      self.rollback = False
      softexit.trigger()