def write_results(self):
# Save results from the Optimizer to HDF5 file if requested
h5_group = get_h5_group(self.h5_fn, self.h5_group_name)
# Some attributes never change and are only set in the first cycle
if self.cur_cycle == 0:
h5_group.attrs["is_cos"] = self.is_cos
try:
atoms = self.geometry.images[0].atoms
coord_size = self.geometry.images[0].coords.size
except AttributeError:
atoms = self.geometry.atoms
coord_size = self.geometry.coords.size
h5_group.attrs["atoms"] = atoms
h5_group.attrs["coord_type"] = self.geometry.coord_type
h5_group.attrs["coord_size"] = coord_size
# Update changing attributes
h5_group.attrs["cur_cycle"] = self.cur_cycle
h5_group.attrs["is_converged"] = self.is_converged
for key, shape in self.data_model.items():
value = getattr(self, key)
# Don't try to set empty values, e.g. 'tangents' are only present
# for COS methods. 'modified_forces' are only present for NCOptimizer.
if not value:
continue
if len(shape) > 1:
h5_group[key][self.cur_cycle, :len(value[-1])] = value[-1]
else:
h5_group[key][self.cur_cycle] = value[-1]
h5_group.file.close()
def init_h5_group(self, atoms, max_cycles=None):
if max_cycles is None:
max_cycles = self.h5_cycles
self.data_model = get_data_model(atoms, max_cycles)
self.h5_group = get_h5_group(self.h5_fn,
self.h5_group_name,
self.data_model,
reset=True)
def set_restart_info(self, restart_info):
# Set restart information general to all optimizers
self.last_cycle = restart_info["last_cycle"] + 1
must_resize = self.last_cycle >= self.max_cycles
if must_resize:
self.max_cycles += restart_info["max_cycles"]
# Resize HDF5
if self.dump:
h5_group = get_h5_group(self.h5_fn, self.h5_group_name)
resize_h5_group(h5_group, self.max_cycles)
h5_group.file.close()
self.coords = [np.array(coords) for coords in restart_info["coords"]]
self.energies = restart_info["energies"]
self.forces = [np.array(forces) for forces in restart_info["forces"]]
self.steps = [np.array(step) for step in restart_info["steps"]]
# Set subclass specific information
self._set_opt_restart_info(restart_info)
# Propagate restart information downwards to the geometry
self.geometry.set_restart_info(restart_info["geom_info"])
def __init__(
self,
geometry,
thresh="gau_loose",
max_step=0.04,
max_cycles=50,
rms_force=None,
rms_force_only=False,
align=False,
dump=False,
dump_restart=None,
prefix="",
reparam_thresh=1e-3,
overachieve_factor=0.0,
restart_info=None,
check_coord_diffs=True,
coord_diff_thresh=0.01,
h5_fn="optimization.h5",
h5_group_name="opt",
):
assert thresh in self.CONV_THRESHS.keys()
self.geometry = geometry
self.thresh = thresh
self.max_step = max_step
self.rms_force_only = rms_force_only
self.align = align
self.dump = dump
self.dump_restart = dump_restart
self.prefix = f"{prefix}_" if prefix else prefix
self.reparam_thresh = reparam_thresh
self.overachieve_factor = float(overachieve_factor)
self.check_coord_diffs = check_coord_diffs
self.coord_diff_thresh = float(coord_diff_thresh)
self.is_cos = issubclass(type(self.geometry), ChainOfStates)
self.convergence = self.make_conv_dict(thresh, rms_force)
for key, value in self.convergence.items():
setattr(self, key, value)
# Setting some default values
self.resetted = False
self.max_cycles = max_cycles
self.out_dir = os.getcwd()
if self.is_cos:
moving_image_num = len(self.geometry.moving_indices)
print(f"Path with {moving_image_num} moving images.")
self.out_dir = Path(self.out_dir)
if not self.out_dir.exists():
os.mkdir(self.out_dir)
self.h5_fn = self.out_dir / h5_fn
self.h5_group_name = h5_group_name
current_fn = "current_geometries.trj" if self.is_cos else "current_geometry.xyz"
self.current_fn = self.get_path_for_fn(current_fn)
final_fn = "final_geometries.trj" if self.is_cos else "final_geometry.xyz"
self.final_fn = self.get_path_for_fn(final_fn)
self.hei_trj_fn = self.get_path_for_fn("cos_hei.trj")
try:
os.remove(self.hei_trj_fn)
except FileNotFoundError:
pass
self.logger = logging.getLogger("optimizer")
# Setting some empty lists as default. The actual shape of the respective
# entries is not considered, which gives us some flexibility.
self.data_model = get_data_model(self.geometry, self.is_cos,
self.max_cycles)
for la in self.data_model.keys():
setattr(self, la, list())
if self.dump:
out_trj_fn = self.get_path_for_fn("optimization.trj")
self.out_trj_handle = open(out_trj_fn, "w")
# Call with reset=True to delete remnants of previous calculations, unless
# the optimizer was restarted. Given a previous optimization with, e.g. 30
# cycles and a second restarted optimization with 20 cycles the last 10 cycles
# of the previous optimization would still be present.
reset = restart_info is None
h5_group = get_h5_group(self.h5_fn,
self.h5_group_name,
self.data_model,
reset=reset)
h5_group.file.close()
if self.prefix:
self.log(f"Created optimizer with prefix {self.prefix}")
self.restarted = False
self.last_cycle = 0
self.cur_cycle = 0
if restart_info is not None:
if isinstance(restart_info, str):
restart_info = yaml.load(restart_info, Loader=yaml.SafeLoader)
self.set_restart_info(restart_info)
self.restarted = True
