def __init__(self,
max_analytic_order=0,
property=Energy,
deltas=None,
details=None,
run_now=False,
queue=None,
robustness=2,
**kwargs):
kwargs.update(property=property)
super(FiniteDifferenceForceField, self).__init__(**kwargs)
self.robustness = robustness
self.max_analytic_order = max_analytic_order
self.filled_for_order = [False] * self.max_order
if max_analytic_order > 1:
raise NotImplementedError
elif max_analytic_order > 0:
if isinstance(self.representation, CartesianRepresentation):
self.computed_property = PropertyDerivative(
property,
representation=self.representation,
order=max_analytic_order)
self.computed_property = self.computed_property.in_units(
self.property_units /
self.representation.units**self.max_analytic_order)
else:
# TODO figure out why I need to do finite difference of cartesian gradients and then convert to get the correct answer (rather than finite difference of internal coordinate gradients)
self.computed_property = PropertyDerivative(
property,
representation=self.representation.molecule.
cartesian_representation,
order=max_analytic_order)
self.computed_property = self.computed_property.in_units(
self.property_units /
(self.representation.molecule.cartesian_representation.
units**self.max_analytic_order))
else:
self.computed_property = property.in_units(self.property_units)
self.displacement_manager = DisplacementManager(
self.representation,
differentiable=self.computed_property,
deltas=deltas,
details=details)
if queue is not None:
self.computation_queue = queue
queue.enqueue(*self.needed_computations)
if run_now:
queue.run()
self.fill()
elif run_now:
self.fill()
def __init__(self,
max_analytic_order=0,
property=Energy,
deltas=None,
details=None,
run_now=False,
queue=None,
robustness=2,
**kwargs):
kwargs.update(property=property)
super(FiniteDifferenceForceField, self).__init__(**kwargs)
self.robustness = robustness
self.max_analytic_order = max_analytic_order
self.filled_for_order = [False] * self.max_order
if max_analytic_order > 1:
raise NotImplementedError
elif max_analytic_order > 0:
if isinstance(self.representation, CartesianRepresentation):
self.computed_property = PropertyDerivative(
property,
representation=self.representation,
order=max_analytic_order
)
self.computed_property = self.computed_property.in_units(
self.property_units / self.representation.units**self.max_analytic_order
)
else:
# TODO figure out why I need to do finite difference of cartesian gradients and then convert to get the correct answer (rather than finite difference of internal coordinate gradients)
self.computed_property = PropertyDerivative(
property,
representation=self.representation.molecule.cartesian_representation,
order=max_analytic_order
)
self.computed_property = self.computed_property.in_units(
self.property_units / (
self.representation.molecule.cartesian_representation.units**self.max_analytic_order
)
)
else:
self.computed_property = property.in_units(self.property_units)
self.displacement_manager = DisplacementManager(
self.representation,
differentiable=self.computed_property,
deltas=deltas,
details=details
)
if queue is not None:
self.computation_queue = queue
queue.enqueue(*self.needed_computations)
if run_now:
queue.run()
self.fill()
elif run_now:
self.fill()
class FiniteDifferenceForceField(ForceField):
"""
"""
##############
# Attributes #
##############
displacement_manager = None
filled_for_order = None
computed_property = None
max_analytic_order = None
robustness = None
computation_queue = None
######################
# Private Attributes #
######################
_unit_dict = None
##################
# Initialization #
##################
def __init__(self,
max_analytic_order=0,
property=Energy,
deltas=None,
details=None,
run_now=False,
queue=None,
robustness=2,
**kwargs):
kwargs.update(property=property)
super(FiniteDifferenceForceField, self).__init__(**kwargs)
self.robustness = robustness
self.max_analytic_order = max_analytic_order
self.filled_for_order = [False] * self.max_order
if max_analytic_order > 1:
raise NotImplementedError
elif max_analytic_order > 0:
if isinstance(self.representation, CartesianRepresentation):
self.computed_property = PropertyDerivative(
property,
representation=self.representation,
order=max_analytic_order
)
self.computed_property = self.computed_property.in_units(
self.property_units / self.representation.units**self.max_analytic_order
)
else:
# TODO figure out why I need to do finite difference of cartesian gradients and then convert to get the correct answer (rather than finite difference of internal coordinate gradients)
self.computed_property = PropertyDerivative(
property,
representation=self.representation.molecule.cartesian_representation,
order=max_analytic_order
)
self.computed_property = self.computed_property.in_units(
self.property_units / (
self.representation.molecule.cartesian_representation.units**self.max_analytic_order
)
)
else:
self.computed_property = property.in_units(self.property_units)
self.displacement_manager = DisplacementManager(
self.representation,
differentiable=self.computed_property,
deltas=deltas,
details=details
)
if queue is not None:
self.computation_queue = queue
queue.enqueue(*self.needed_computations)
if run_now:
queue.run()
self.fill()
elif run_now:
self.fill()
##############
# Properties #
##############
@property
def all_computations(self):
all_comps = set()
for order in xrange(self.max_analytic_order+1, self.max_order+1):
for coords in symmetric_product(self.representation, order-self.max_analytic_order):
fd = FiniteDifferenceDerivative(
self.displacement_manager,
*coords,
target_robustness=self.robustness
)
for incs in fd.needed_increments:
increments = [0] * len(self.representation)
for coord, inc in zip(fd.variables, incs):
increments[coord.index] = inc
dmol = self.displacement_manager.displacement_for(tuple(increments)).displaced_molecule
comp = dmol.get_computation_for_property(
self.computed_property,
self.displacement_manager.details
)
if comp not in all_comps:
all_comps.add(comp)
if self.max_analytic_order == 1:
comp = self.base_molecule.get_computation_for_property(
self.computed_property,
self.displacement_manager.details
)
if comp not in all_comps:
all_comps.add(comp)
elif self.max_analytic_order > 2:
# TODO figure out how many analytic computations need to be done
raise NotImplementedError
return list(all_comps)
@property
def needed_computations(self):
return [c for c in self.all_computations if not c.completed]
###########
# Methods #
###########
def fill(self):
if self.computation_queue is not None and not self.computation_queue.is_finished():
self.computation_queue.run()
for n in xrange(self.max_order):
self.fill_order(self.max_order - n)
def fill_order(self, order):
if self.filled_for_order[order-1]:
return
#----------------------------------------#
tens = self.tensors[order]
if order <= self.max_analytic_order:
#TODO this will need to be significantly modified for hessians and higher, since the transformation to interal coordinates requires all lower derivatives as well...
tens[...] = self.base_molecule.get_property(
PropertyDerivative(self.molecular_property, order) if order > 0 else self.molecular_property,
details=self.displacement_manager.details
).value.in_representation(self.representation)
#----------------------------------------#
else:
for f_coords in symmetric_product(self.representation, order-self.max_analytic_order):
if self.max_analytic_order == 0 or isinstance(self.representation, CartesianRepresentation):
spread_val = FiniteDifferenceDerivative(
self.displacement_manager,
*f_coords,
target_robustness=self.robustness
).value
else:
spread_val = RepresentationDependentTensor(
FiniteDifferenceDerivative(
self.displacement_manager,
*f_coords,
target_robustness=self.robustness
).value,
representation=self.representation.molecule.cartesian_representation
)
spread_val = spread_val.in_representation(self.representation)
for perm in permutations(f_coords):
tens[perm] = spread_val
#----------------------------------------#
self.filled_for_order[order-1] = True
@typechecked(order=int)
def for_order(self, order):
if order <= self.max_order and not self.filled_for_order[order-1]:
raise ValueError("Order {} tensor of ForceField instance not yet computed.".format(
order
))
else:
return super(FiniteDifferenceForceField, self).for_order(order)
class FiniteDifferenceForceField(ForceField):
"""
"""
##############
# Attributes #
##############
displacement_manager = None
filled_for_order = None
computed_property = None
max_analytic_order = None
robustness = None
computation_queue = None
######################
# Private Attributes #
######################
_unit_dict = None
##################
# Initialization #
##################
def __init__(self,
max_analytic_order=0,
property=Energy,
deltas=None,
details=None,
run_now=False,
queue=None,
robustness=2,
**kwargs):
kwargs.update(property=property)
super(FiniteDifferenceForceField, self).__init__(**kwargs)
self.robustness = robustness
self.max_analytic_order = max_analytic_order
self.filled_for_order = [False] * self.max_order
if max_analytic_order > 1:
raise NotImplementedError
elif max_analytic_order > 0:
if isinstance(self.representation, CartesianRepresentation):
self.computed_property = PropertyDerivative(
property,
representation=self.representation,
order=max_analytic_order)
self.computed_property = self.computed_property.in_units(
self.property_units /
self.representation.units**self.max_analytic_order)
else:
# TODO figure out why I need to do finite difference of cartesian gradients and then convert to get the correct answer (rather than finite difference of internal coordinate gradients)
self.computed_property = PropertyDerivative(
property,
representation=self.representation.molecule.
cartesian_representation,
order=max_analytic_order)
self.computed_property = self.computed_property.in_units(
self.property_units /
(self.representation.molecule.cartesian_representation.
units**self.max_analytic_order))
else:
self.computed_property = property.in_units(self.property_units)
self.displacement_manager = DisplacementManager(
self.representation,
differentiable=self.computed_property,
deltas=deltas,
details=details)
if queue is not None:
self.computation_queue = queue
queue.enqueue(*self.needed_computations)
if run_now:
queue.run()
self.fill()
elif run_now:
self.fill()
##############
# Properties #
##############
@property
def all_computations(self):
all_comps = set()
for order in xrange(self.max_analytic_order + 1, self.max_order + 1):
for coords in symmetric_product(self.representation,
order - self.max_analytic_order):
fd = FiniteDifferenceDerivative(
self.displacement_manager,
*coords,
target_robustness=self.robustness)
for incs in fd.needed_increments:
increments = [0] * len(self.representation)
for coord, inc in zip(fd.variables, incs):
increments[coord.index] = inc
dmol = self.displacement_manager.displacement_for(
tuple(increments)).displaced_molecule
comp = dmol.get_computation_for_property(
self.computed_property,
self.displacement_manager.details)
if comp not in all_comps:
all_comps.add(comp)
if self.max_analytic_order == 1:
comp = self.base_molecule.get_computation_for_property(
self.computed_property, self.displacement_manager.details)
if comp not in all_comps:
all_comps.add(comp)
elif self.max_analytic_order > 2:
# TODO figure out how many analytic computations need to be done
raise NotImplementedError
return list(all_comps)
@property
def needed_computations(self):
return [c for c in self.all_computations if not c.completed]
###########
# Methods #
###########
def fill(self):
if self.computation_queue is not None and not self.computation_queue.is_finished(
):
self.computation_queue.run()
for n in xrange(self.max_order):
self.fill_order(self.max_order - n)
def fill_order(self, order):
if self.filled_for_order[order - 1]:
return
#----------------------------------------#
tens = self.tensors[order]
if order <= self.max_analytic_order:
#TODO this will need to be significantly modified for hessians and higher, since the transformation to interal coordinates requires all lower derivatives as well...
tens[...] = self.base_molecule.get_property(
PropertyDerivative(self.molecular_property, order)
if order > 0 else self.molecular_property,
details=self.displacement_manager.details
).value.in_representation(self.representation)
#----------------------------------------#
else:
for f_coords in symmetric_product(self.representation,
order - self.max_analytic_order):
if self.max_analytic_order == 0 or isinstance(
self.representation, CartesianRepresentation):
spread_val = FiniteDifferenceDerivative(
self.displacement_manager,
*f_coords,
target_robustness=self.robustness).value
else:
spread_val = RepresentationDependentTensor(
FiniteDifferenceDerivative(
self.displacement_manager,
*f_coords,
target_robustness=self.robustness).value,
representation=self.representation.molecule.
cartesian_representation)
spread_val = spread_val.in_representation(
self.representation)
for perm in permutations(f_coords):
tens[perm] = spread_val
#----------------------------------------#
self.filled_for_order[order - 1] = True
@typechecked(order=int)
def for_order(self, order):
if order <= self.max_order and not self.filled_for_order[order - 1]:
raise ValueError(
"Order {} tensor of ForceField instance not yet computed.".
format(order))
else:
return super(FiniteDifferenceForceField, self).for_order(order)
