"""Structure optimization. """
import time
from math import sqrt
from os.path import isfile
from ase.io.jsonio import read_json, write_json
from ase.calculators.calculator import PropertyNotImplementedError
from ase.parallel import world, barrier
from ase.io.trajectory import Trajectory
from ase.utils import IOContext
import collections.abc
class RestartError(RuntimeError):
pass
class Dynamics(IOContext):
"""Base-class for all MD and structure optimization classes."""
def __init__(
self, atoms, logfile, trajectory, append_trajectory=False, master=None
):
"""Dynamics object.
Parameters:
atoms: Atoms object
The Atoms object to operate on.
logfile: file object or str
If *logfile* is a string, a file with that name will be opened.
Use '-' for stdout.
trajectory: Trajectory object or str
Attach trajectory object. If *trajectory* is a string a
Trajectory will be constructed. Use *None* for no
trajectory.
append_trajectory: boolean
Defaults to False, which causes the trajectory file to be
overwriten each time the dynamics is restarted from scratch.
If True, the new structures are appended to the trajectory
file instead.
master: boolean
Defaults to None, which causes only rank 0 to save files. If
set to true, this rank will save files.
"""
self.atoms = atoms
self.logfile = self.openfile(logfile, mode='a', comm=world)
self.observers = []
self.nsteps = 0
# maximum number of steps placeholder with maxint
self.max_steps = 100000000
if trajectory is not None:
if isinstance(trajectory, str):
mode = "a" if append_trajectory else "w"
trajectory = self.closelater(Trajectory(
trajectory, mode=mode, atoms=atoms, master=master
))
self.attach(trajectory)
def get_number_of_steps(self):
return self.nsteps
def insert_observer(
self, function, position=0, interval=1, *args, **kwargs
):
"""Insert an observer."""
if not isinstance(function, collections.abc.Callable):
function = function.write
self.observers.insert(position, (function, interval, args, kwargs))
def attach(self, function, interval=1, *args, **kwargs):
"""Attach callback function.
If *interval > 0*, at every *interval* steps, call *function* with
arguments *args* and keyword arguments *kwargs*.
If *interval <= 0*, after step *interval*, call *function* with
arguments *args* and keyword arguments *kwargs*. This is
currently zero indexed."""
if hasattr(function, "set_description"):
d = self.todict()
d.update(interval=interval)
function.set_description(d)
if not hasattr(function, "__call__"):
function = function.write
self.observers.append((function, interval, args, kwargs))
def call_observers(self):
for function, interval, args, kwargs in self.observers:
call = False
# Call every interval iterations
if interval > 0:
if (self.nsteps % interval) == 0:
call = True
# Call only on iteration interval
elif interval <= 0:
if self.nsteps == abs(interval):
call = True
if call:
function(*args, **kwargs)
[docs] def irun(self):
"""Run dynamics algorithm as generator. This allows, e.g.,
to easily run two optimizers or MD thermostats at the same time.
Examples:
>>> opt1 = BFGS(atoms)
>>> opt2 = BFGS(StrainFilter(atoms)).irun()
>>> for _ in opt2:
>>> opt1.run()
"""
# compute initial structure and log the first step
self.atoms.get_forces()
# yield the first time to inspect before logging
yield False
if self.nsteps == 0:
self.log()
self.call_observers()
# run the algorithm until converged or max_steps reached
while not self.converged() and self.nsteps < self.max_steps:
# compute the next step
self.step()
self.nsteps += 1
# let the user inspect the step and change things before logging
# and predicting the next step
yield False
# log the step
self.log()
self.call_observers()
# finally check if algorithm was converged
yield self.converged()
def run(self):
"""Run dynamics algorithm.
This method will return when the forces on all individual
atoms are less than *fmax* or when the number of steps exceeds
*steps*."""
for converged in Dynamics.irun(self):
pass
return converged
def converged(self, *args):
"""" a dummy function as placeholder for a real criterion, e.g. in
Optimizer """
return False
[docs] def log(self, *args):
""" a dummy function as placeholder for a real logger, e.g. in
Optimizer """
return True
def step(self):
"""this needs to be implemented by subclasses"""
raise RuntimeError("step not implemented.")
class Optimizer(Dynamics):
"""Base-class for all structure optimization classes."""
# default maxstep for all optimizers
defaults = {'maxstep': 0.2}
def __init__(
self,
atoms,
restart,
logfile,
trajectory,
master=None,
append_trajectory=False,
force_consistent=False,
):
"""Structure optimizer object.
Parameters:
atoms: Atoms object
The Atoms object to relax.
restart: str
Filename for restart file. Default value is *None*.
logfile: file object or str
If *logfile* is a string, a file with that name will be opened.
Use '-' for stdout.
trajectory: Trajectory object or str
Attach trajectory object. If *trajectory* is a string a
Trajectory will be constructed. Use *None* for no
trajectory.
master: boolean
Defaults to None, which causes only rank 0 to save files. If
set to true, this rank will save files.
append_trajectory: boolean
Appended to the trajectory file instead of overwriting it.
force_consistent: boolean or None
Use force-consistent energy calls (as opposed to the energy
extrapolated to 0 K). If force_consistent=None, uses
force-consistent energies if available in the calculator, but
falls back to force_consistent=False if not.
"""
Dynamics.__init__(
self,
atoms,
logfile,
trajectory,
append_trajectory=append_trajectory,
master=master,
)
self.force_consistent = force_consistent
if self.force_consistent is None:
self.set_force_consistent()
self.restart = restart
# initialize attribute
self.fmax = None
if restart is None or not isfile(restart):
self.initialize()
else:
self.read()
barrier()
def todict(self):
description = {
"type": "optimization",
"optimizer": self.__class__.__name__,
}
return description
def initialize(self):
pass
def irun(self, fmax=0.05, steps=None):
""" call Dynamics.irun and keep track of fmax"""
self.fmax = fmax
if steps:
self.max_steps = steps
return Dynamics.irun(self)
def run(self, fmax=0.05, steps=None):
""" call Dynamics.run and keep track of fmax"""
self.fmax = fmax
if steps:
self.max_steps = steps
return Dynamics.run(self)
def converged(self, forces=None):
"""Did the optimization converge?"""
if forces is None:
forces = self.atoms.get_forces()
if hasattr(self.atoms, "get_curvature"):
return (forces ** 2).sum(
axis=1
).max() < self.fmax ** 2 and self.atoms.get_curvature() < 0.0
return (forces ** 2).sum(axis=1).max() < self.fmax ** 2
def log(self, forces=None):
if forces is None:
forces = self.atoms.get_forces()
fmax = sqrt((forces ** 2).sum(axis=1).max())
e = self.atoms.get_potential_energy(
force_consistent=self.force_consistent
)
T = time.localtime()
if self.logfile is not None:
name = self.__class__.__name__
if self.nsteps == 0:
args = (" " * len(name), "Step", "Time", "Energy", "fmax")
msg = "%s %4s %8s %15s %12s\n" % args
self.logfile.write(msg)
if self.force_consistent:
msg = "*Force-consistent energies used in optimization.\n"
self.logfile.write(msg)
ast = {1: "*", 0: ""}[self.force_consistent]
args = (name, self.nsteps, T[3], T[4], T[5], e, ast, fmax)
msg = "%s: %3d %02d:%02d:%02d %15.6f%1s %12.4f\n" % args
self.logfile.write(msg)
self.logfile.flush()
def dump(self, data):
if world.rank == 0 and self.restart is not None:
with open(self.restart, 'w') as fd:
write_json(fd, data)
def load(self):
with open(self.restart) as fd:
try:
return read_json(fd, always_array=False)
except Exception as ex:
msg = ('Could not decode restart file as JSON. '
f'You may need to delete the restart file {self.restart}')
raise RestartError(msg) from ex
def set_force_consistent(self):
"""Automatically sets force_consistent to True if force_consistent
energies are supported by calculator; else False."""
try:
self.atoms.get_potential_energy(force_consistent=True)
except PropertyNotImplementedError:
self.force_consistent = False
else:
self.force_consistent = True