#!/usr/bin/env python
#=============================================================================================
# MODULE DOCSTRING
#=============================================================================================
"""
Parameter handlers for the SMIRNOFF force field engine
This file contains standard parameter handlers for the SMIRNOFF force field engine.
These classes implement the object model for self-contained parameter assignment.
New pluggable handlers can be created by creating subclasses of :class:`ParameterHandler`.
"""
__all__ = [
'SMIRNOFFSpecError',
'IncompatibleParameterError',
'UnassignedValenceParameterException',
'UnassignedBondParameterException',
'UnassignedAngleParameterException',
'NonbondedMethod',
'ParameterList',
'ParameterType',
'ParameterHandler',
'ParameterAttribute',
'IndexedParameterAttribute',
'ConstraintHandler',
'BondHandler',
'AngleHandler',
'ProperTorsionHandler',
'ImproperTorsionHandler',
'vdWHandler'
]
#=============================================================================================
# GLOBAL IMPORTS
#=============================================================================================
import copy
from collections import OrderedDict
from enum import Enum
import functools
import inspect
import logging
from simtk import openmm, unit
from openforcefield.utils import attach_units, \
extract_serialized_units_from_dict, ToolkitUnavailableException, MessageException, \
object_to_quantity
from openforcefield.topology import ValenceDict, ImproperDict
from openforcefield.typing.chemistry import ChemicalEnvironment
from openforcefield.utils import IncompatibleUnitError
from openforcefield.utils.collections import ValidatedList
#=============================================================================================
# CONFIGURE LOGGER
#=============================================================================================
logger = logging.getLogger(__name__)
#======================================================================
# CUSTOM EXCEPTIONS
#======================================================================
class SMIRNOFFSpecError(MessageException):
"""
Exception for when data is noncompliant with the SMIRNOFF data specification.
"""
pass
class IncompatibleParameterError(MessageException):
"""
Exception for when a set of parameters is scientifically/technically incompatible with another
"""
pass
class UnassignedValenceParameterException(Exception):
"""Exception raised when there are valence terms for which a ParameterHandler can't find parameters."""
pass
class UnassignedBondParameterException(UnassignedValenceParameterException):
"""Exception raised when there are bond terms for which a ParameterHandler can't find parameters."""
pass
class UnassignedAngleParameterException(UnassignedValenceParameterException):
"""Exception raised when there are angle terms for which a ParameterHandler can't find parameters."""
pass
class UnassignedProperTorsionParameterException(UnassignedValenceParameterException):
"""Exception raised when there are proper torsion terms for which a ParameterHandler can't find parameters."""
pass
#======================================================================
# ENUM TYPES
#======================================================================
class NonbondedMethod(Enum):
"""
An enumeration of the nonbonded methods
"""
NoCutoff = 0
CutoffPeriodic = 1
CutoffNonPeriodic = 2
Ewald = 3
PME = 4
#======================================================================
# PARAMETER ATTRIBUTES
#======================================================================
# TODO: Think about adding attrs to the dependencies and inherit from attr.ib
[docs]class ParameterAttribute:
"""A descriptor for ``ParameterType`` attributes.
The descriptors allows associating to the parameter a default value,
which makes the attribute optional, a unit, and a custom converter.
Because we may want to have ``None`` as a default value, required
attributes have the ``default`` set to the special type ``UNDEFINED``.
Converters can be both static or instance functions/methods with
respective signatures
converter(value): -> converted_value
converter(instance, parameter_attribute, value): -> converted_value
A decorator syntax is available (see example below).
Parameters
----------
default : object, optional
When specified, the descriptor makes this attribute optional by
attaching a default value to it.
unit : simtk.unit.Quantity, optional
When specified, only quantities with compatible units are allowed
to be set, and string expressions are automatically parsed into a
``Quantity``.
converter : callable, optional
An optional function that can be used to convert values before
setting the attribute.
See Also
--------
IndexedParameterAttribute
A parameter attribute with multiple terms.
Examples
--------
Create a parameter type with an optional and a required attribute.
>>> class MyParameter:
... attr_required = ParameterAttribute()
... attr_optional = ParameterAttribute(default=2)
...
>>> my_par = MyParameter()
Even without explicit assignment, the default value is returned.
>>> my_par.attr_optional
2
If you try to access an attribute without setting it first, an
exception is raised.
>>> my_par.attr_required
Traceback (most recent call last):
...
AttributeError: 'MyParameter' object has no attribute '_attr_required'
The attribute allow automatic conversion and validation of units.
>>> from simtk import unit
>>> class MyParameter:
... attr_quantity = ParameterAttribute(unit=unit.angstrom)
...
>>> my_par = MyParameter()
>>> my_par.attr_quantity = '1.0 * nanometer'
>>> my_par.attr_quantity
Quantity(value=1.0, unit=nanometer)
>>> my_par.attr_quantity = 3.0
Traceback (most recent call last):
...
openforcefield.utils.utils.IncompatibleUnitError: attr_quantity=3.0 dimensionless should have units of angstrom
You can attach a custom converter to an attribute.
>>> class MyParameter:
... # Both strings and integers convert nicely to floats with float().
... attr_all_to_float = ParameterAttribute(converter=float)
... attr_int_to_float = ParameterAttribute()
... @attr_int_to_float.converter
... def attr_int_to_float(self, attr, value):
... # This converter converts only integers to float
... # and raise an exception for the other types.
... if isinstance(value, int):
... return float(value)
... elif not isinstance(value, float):
... raise TypeError(f"Cannot convert '{value}' to float")
... return value
...
>>> my_par = MyParameter()
attr_all_to_float accepts and convert to float both strings and integers
>>> my_par.attr_all_to_float = 1
>>> my_par.attr_all_to_float
1.0
>>> my_par.attr_all_to_float = '2.0'
>>> my_par.attr_all_to_float
2.0
The custom converter associated to attr_int_to_float converts only integers instead.
>>> my_par.attr_int_to_float = 3
>>> my_par.attr_int_to_float
3.0
>>> my_par.attr_int_to_float = '4.0'
Traceback (most recent call last):
...
TypeError: Cannot convert '4.0' to float
"""
[docs] class UNDEFINED:
"""Custom type used by ``ParameterAttribute`` to differentiate between ``None`` and undeclared default."""
pass
[docs] def __init__(self, default=UNDEFINED, unit=None, converter=None):
self.default = default
self._unit = unit
self._converter = converter
def __set_name__(self, owner, name):
self._name = '_' + name
@property
def name(self):
# Get rid of the initial underscore.
return self._name[1:]
def __get__(self, instance, owner):
if instance is None:
# This is called from the class. Return the descriptor object.
return self
try:
return getattr(instance, self._name)
except AttributeError:
# The attribute has not initialized. Check if there's a default.
if self.default is ParameterAttribute.UNDEFINED:
raise
return self.default
def __set__(self, instance, value):
# Convert and validate the value.
value = self._convert_and_validate(instance, value)
setattr(instance, self._name, value)
[docs] def converter(self, converter):
"""Create a new ParameterAttribute with an associated converter.
This is meant to be used as a decorator (see main examples).
"""
return self.__class__(default=self.default, converter=converter)
def _convert_and_validate(self, instance, value):
"""Convert to Quantity, validate units, and call custom converter."""
# The default value is always allowed.
if self._is_valid_default(value):
return value
# Convert and validate units.
value = self._validate_units(value)
# Call the custom converter before setting the value.
value = self._call_converter(value, instance)
return value
def _is_valid_default(self, value):
"""Return True if this is a defined default value."""
return self.default is not ParameterAttribute.UNDEFINED and value == self.default
def _validate_units(self, value):
"""Convert strings expressions to Quantity and validate the units if requested."""
if self._unit is not None:
# Convert eventual strings to Quantity objects.
value = object_to_quantity(value)
# Check if units are compatible.
try:
if not self._unit.is_compatible(value.unit):
raise IncompatibleUnitError(f'{self.name}={value} should have units of {self._unit}')
except AttributeError:
# This is not a Quantity object.
raise IncompatibleUnitError(f'{self.name}={value} should have units of {self._unit}')
return value
def _call_converter(self, value, instance):
"""Correctly calls static and instance converters."""
if self._converter is not None:
try:
# Static function.
return self._converter(value)
except TypeError:
# Instance method.
return self._converter(instance, self, value)
return value
[docs]class IndexedParameterAttribute(ParameterAttribute):
"""The attribute of a parameter with an unspecified number of terms.
Some parameters can be associated to multiple terms, For example,
torsions have parameters such as k1, k2, ..., and ``IndexedParameterAttribute``
can be used to encapsulate the sequence of terms.
The only substantial difference with ``ParameterAttribute`` is that
only sequences are supported as values and converters and units are
checked on each element of the sequence.
Currently, the descriptor makes the sequence immutable. This is to
avoid that an element of the sequence could be set without being
properly validated. In the future, the data could be wrapped in a
safe list that would safely allow mutability.
Parameters
----------
default : object, optional
When specified, the descriptor makes this attribute optional by
attaching a default value to it.
unit : simtk.unit.Quantity, optional
When specified, only sequences of quantities with compatible units
are allowed to be set.
converter : callable, optional
An optional function that can be used to validate and cast each
element of the sequence before setting the attribute.
See Also
--------
ParameterAttribute
A simple parameter attribute.
Examples
--------
Create an optional indexed attribute with unit of angstrom.
>>> from simtk import unit
>>> class MyParameter:
... length = IndexedParameterAttribute(default=None, unit=unit.angstrom)
...
>>> my_par = MyParameter()
>>> my_par.length is None
True
Strings are parsed into Quantity objects.
>>> my_par.length = ['1 * angstrom', 0.5 * unit.nanometer]
>>> my_par.length[0]
Quantity(value=1, unit=angstrom)
Similarly, custom converters work as with ``ParameterAttribute``, but
they are used to validate each value in the sequence.
>>> class MyParameter:
... attr_indexed = IndexedParameterAttribute(converter=float)
...
>>> my_par = MyParameter()
>>> my_par.attr_indexed = [1, '1.0', '1e-2', 4.0]
>>> my_par.attr_indexed
[1.0, 1.0, 0.01, 4.0]
"""
def _convert_and_validate(self, instance, value):
"""Overwrite ParameterAttribute._convert_and_validate to make the value a ValidatedList."""
# The default value is always allowed.
if self._is_valid_default(value):
return value
# We push the converters into a ValidatedList so that we can make
# sure that elements are validated correctly when they are modified
# after their initialization.
# ValidatedList expects converters that take the value as a single
# argument so we create a partial function with the instance assigned.
static_converter = functools.partial(self._call_converter, instance=instance)
value = ValidatedList(value, converter=[self._validate_units, static_converter])
return value
class _ParameterAttributeInitializer:
"""A base class for ``ParameterType`` and ``ParameterHandler`` objects.
Encapsulate shared code of ``ParameterType`` and ``ParameterHandler``.
In particular, this base class provides an ``__init__`` method that
automatically initialize the attributes defined through the ``ParameterAttribute``
and ``IndexedParameterAttribute`` descriptors, as well as handling
cosmetic attributes.
See Also
--------
ParameterAttribute
A simple parameter attribute.
IndexedParameterAttribute
A parameter attribute with multiple terms.
"""
def __init__(self, allow_cosmetic_attributes=False, **kwargs):
"""
Initialize parameter and cosmetic attributes.
Parameters
----------
allow_cosmetic_attributes : bool optional. Default = False
Whether to permit non-spec kwargs ("cosmetic attributes").
If True, non-spec kwargs will be stored as an attribute of
this parameter which can be accessed and written out. Otherwise,
an exception will be raised.
"""
# A list that may be populated to record the cosmetic attributes
# read from a SMIRNOFF data source.
self._cosmetic_attribs = []
# Do not modify the original data.
smirnoff_data = copy.deepcopy(kwargs)
# Check for indexed attributes and stack them into a list.
# Keep track of how many indexed attribute we find to make sure they all have the same length.
indexed_attr_lengths = {}
for attrib_basename in self._get_indexed_parameter_attributes().keys():
index = 1
while True:
attrib_w_index = '{}{}'.format(attrib_basename, index)
# Exit the while loop if the indexed attribute is not given.
try:
attrib_w_index_value = smirnoff_data[attrib_w_index]
except KeyError:
break
# Check if this is the first iteration.
if index == 1:
# Check if this attribute has been specified with and without index.
if attrib_basename in smirnoff_data:
err_msg = (f"The attribute '{attrib_basename}' has been specified "
f"with and without index: '{attrib_w_index}'")
raise TypeError(err_msg)
# Otherwise create the list object.
smirnoff_data[attrib_basename] = list()
# Append the new value to the list.
smirnoff_data[attrib_basename].append(attrib_w_index_value)
# Remove the indexed attribute from the kwargs as it will
# be exposed only as an element of the list.
del smirnoff_data[attrib_w_index]
index += 1
# Update the lengths with this attribute (if it was found).
if index > 1:
indexed_attr_lengths[attrib_basename] = len(smirnoff_data[attrib_basename])
# Raise an error if we there are different indexed
# attributes with a different number of terms.
if len(set(indexed_attr_lengths.values())) > 1:
raise TypeError('The following indexed attributes have '
f'different lengths: {indexed_attr_lengths}')
# Check for missing required arguments.
given_attributes = set(smirnoff_data.keys())
required_attributes = set(self._get_required_parameter_attributes().keys())
missing_attributes = required_attributes.difference(given_attributes)
if len(missing_attributes) != 0:
msg = (f"{self.__class__} require the following missing parameters: {sorted(missing_attributes)}."
f" Defined kwargs are {sorted(smirnoff_data.keys())}")
raise SMIRNOFFSpecError(msg)
# Finally, set attributes of this ParameterType and handle cosmetic attributes.
allowed_attributes = set(self._get_parameter_attributes().keys())
for key, val in smirnoff_data.items():
if key in allowed_attributes:
setattr(self, key, val)
# Handle all unknown kwargs as cosmetic so we can write them back out
elif allow_cosmetic_attributes:
self.add_cosmetic_attribute(key, val)
else:
msg = (f"Unexpected kwarg ({key}: {val}) passed to {self.__class__} constructor. "
"If this is a desired cosmetic attribute, consider setting "
"'allow_cosmetic_attributes=True'")
raise SMIRNOFFSpecError(msg)
def to_dict(self, discard_cosmetic_attributes=False):
"""
Convert this object to dict format.
The returning dictionary contains all the ``ParameterAttribute``
and ``IndexedParameterAttribute`` as well as cosmetic attributes
if ``discard_cosmetic_attributes`` is ``False``.
Parameters
----------
discard_cosmetic_attributes : bool, optional. Default = False
Whether to discard non-spec attributes of this object
Returns
-------
smirnoff_dict : dict
The SMIRNOFF-compliant dict representation of this object.
"""
# Make a list of all attribs that should be included in the
# returned dict (call list() to make a copy). We discard
# optional attributes that are set to None defaults.
attribs_to_return = list(self._get_defined_parameter_attributes().keys())
# Start populating a dict of the attribs.
indexed_attribs = set(self._get_indexed_parameter_attributes().keys())
smirnoff_dict = OrderedDict()
# If attribs_to_return is ordered here, that will effectively be an informal output ordering
for attrib_name in attribs_to_return:
attrib_value = getattr(self, attrib_name)
if attrib_name in indexed_attribs:
for idx, val in enumerate(attrib_value):
smirnoff_dict[attrib_name + str(idx+1)] = val
else:
smirnoff_dict[attrib_name] = attrib_value
# Serialize cosmetic attributes.
if not(discard_cosmetic_attributes):
for cosmetic_attrib in self._cosmetic_attribs:
smirnoff_dict[cosmetic_attrib] = getattr(self, '_' + cosmetic_attrib)
return smirnoff_dict
def add_cosmetic_attribute(self, attr_name, attr_value):
"""
Add a cosmetic attribute to this object.
This attribute will not have a functional effect on the object
in the Open Force Field toolkit, but can be written out during
output.
.. warning :: The API for modifying cosmetic attributes is experimental
and may change in the future (see issue #338).
Parameters
----------
attr_name : str
Name of the attribute to define for this object.
attr_value : str
The value of the attribute to define for this object.
"""
setattr(self, '_'+attr_name, attr_value)
self._cosmetic_attribs.append(attr_name)
def delete_cosmetic_attribute(self, attr_name):
"""
Delete a cosmetic attribute from this object.
.. warning :: The API for modifying cosmetic attributes is experimental
and may change in the future (see issue #338).
Parameters
----------
attr_name : str
Name of the cosmetic attribute to delete.
"""
# TODO: Can we handle this by overriding __delattr__ instead?
# Would we also need to override __del__ as well to cover both deletation methods?
delattr(self, '_'+attr_name)
self._cosmetic_attribs.remove(attr_name)
@classmethod
def _get_parameter_attributes(cls, filter=None):
"""Return all the attributes of the parameters.
This is constructed dynamically by introspection gathering all
the descriptors that are instances of the ParameterAttribute class.
Parent classes of the parameter types are inspected as well.
Note that since Python 3.6 the order of the class attribute definition
is preserved (see PEP 520) so this function will return the attribute
in their declaration order.
Parameters
----------
filter : Callable, optional
An optional function with signature filter(ParameterAttribute) -> bool.
If specified, only attributes for which this functions returns
True are returned.
Returns
-------
parameter_attributes : Dict[str, ParameterAttribute]
A map from the name of the controlled parameter to the
ParameterAttribute descriptor handling it.
Examples
--------
>>> parameter_attributes = ParameterType._get_parameter_attributes()
>>> sorted(parameter_attributes.keys())
['id', 'parent_id', 'smirks']
>>> isinstance(parameter_attributes['id'], ParameterAttribute)
True
"""
# If no filter is specified, get all the parameters.
if filter is None:
filter = lambda x: True
# Go through MRO and retrieve also parents descriptors. The function
# inspect.getmembers() automatically resolves the MRO, but it also
# sorts the attribute alphabetically by name. Here we want the order
# to be the same as the declaration order, which is guaranteed by PEP 520,
# starting from the parent class.
parameter_attributes = OrderedDict((name, descriptor) for c in reversed(inspect.getmro(cls))
for name, descriptor in c.__dict__.items()
if isinstance(descriptor, ParameterAttribute) and filter(descriptor))
return parameter_attributes
@classmethod
def _get_indexed_parameter_attributes(cls):
"""Shortcut to retrieve only IndexedParameterAttributes."""
return cls._get_parameter_attributes(filter=lambda x: isinstance(x, IndexedParameterAttribute))
@classmethod
def _get_required_parameter_attributes(cls):
"""Shortcut to retrieve only required ParameterAttributes."""
return cls._get_parameter_attributes(filter=lambda x: x.default is x.UNDEFINED)
@classmethod
def _get_optional_parameter_attributes(cls):
"""Shortcut to retrieve only required ParameterAttributes."""
return cls._get_parameter_attributes(filter=lambda x: x.default is not x.UNDEFINED)
def _get_defined_parameter_attributes(self):
"""Returns all the attributes except for the optional attributes that have None default value.
This returns first the required attributes and then the defined optional
attribute in their respective declaration order.
"""
required = self._get_required_parameter_attributes()
optional = self._get_optional_parameter_attributes()
# Filter the optional parameters that are set to their default.
optional = OrderedDict((name, descriptor) for name, descriptor in optional.items()
if not(descriptor.default is None and getattr(self, name) == descriptor.default))
required.update(optional)
return required
#======================================================================
# PARAMETER TYPE/LIST
#======================================================================
# We can't actually make this derive from dict, because it's possible for the user to change SMIRKS
# of parameters already in the list, which would cause the ParameterType object's SMIRKS and
# the dictionary key's SMIRKS to be out of sync.
[docs]class ParameterList(list):
"""
Parameter list that also supports accessing items by SMARTS string.
.. warning :: This API is experimental and subject to change.
"""
# TODO: Make this faster by caching SMARTS -> index lookup?
# TODO: Override __del__ to make sure we don't remove root atom type
# TODO: Allow retrieval by `id` as well
[docs] def __init__(self, input_parameter_list=None):
"""
Initialize a new ParameterList, optionally providing a list of ParameterType objects
to initially populate it.
Parameters
----------
input_parameter_list: list[ParameterType], default=None
A pre-existing list of ParameterType-based objects. If None, this ParameterList
will be initialized empty.
"""
super().__init__()
input_parameter_list = input_parameter_list or []
# TODO: Should a ParameterList only contain a single kind of ParameterType?
for input_parameter in input_parameter_list:
self.append(input_parameter)
[docs] def append(self, parameter):
"""
Add a ParameterType object to the end of the ParameterList
Parameters
----------
parameter : a ParameterType object
"""
# TODO: Ensure that newly added parameter is the same type as existing?
super().append(parameter)
[docs] def extend(self, other):
"""
Add a ParameterList object to the end of the ParameterList
Parameters
----------
other : a ParameterList
"""
if not isinstance(other, ParameterList):
msg = 'ParameterList.extend(other) expected instance of ParameterList, ' \
'but received {} (type {}) instead'.format(other, type(other))
raise TypeError(msg)
# TODO: Check if other ParameterList contains the same ParameterTypes?
super().extend(other)
[docs] def index(self, item):
"""
Get the numerical index of a ParameterType object or SMIRKS in this ParameterList. Raises ValueError
if the item is not found.
Parameters
----------
item : ParameterType object or str
The parameter or SMIRKS to look up in this ParameterList
Returns
-------
index : int
The index of the found item
"""
if isinstance(item, ParameterType):
return super().index(item)
else:
for parameter in self:
if parameter.smirks == item:
return self.index(parameter)
raise IndexError('SMIRKS {item} not found in ParameterList'.format(item=item))
[docs] def insert(self, index, parameter):
"""
Add a ParameterType object as if this were a list
Parameters
----------
index : int
The numerical position to insert the parameter at
parameter : a ParameterType object
The parameter to insert
"""
# TODO: Ensure that newly added parameter is the same type as existing?
super().insert(index, parameter)
def __delitem__(self, item):
"""
Delete item by index or SMIRKS.
Parameters
----------
item : str or int
SMIRKS or numerical index of item in this ParameterList
"""
if type(item) is int:
index = item
else:
# Try to find by SMIRKS
index = self.index(item)
super().__delitem__(index)
def __getitem__(self, item):
"""
Retrieve item by index or SMIRKS
Parameters
----------
item : str or int
SMIRKS or numerical index of item in this ParameterList
"""
if type(item) is int:
index = item
elif type(item) is slice:
index = item
else:
index = self.index(item)
return super().__getitem__(index)
# TODO: Override __setitem__ and __del__ to ensure we can slice by SMIRKS as well
def __contains__(self, item):
"""Check to see if either Parameter or SMIRKS is contained in parameter list.
Parameters
----------
item : str
SMIRKS of item in this ParameterList
"""
if isinstance(item, str):
# Special case for SMIRKS strings
if item in [result.smirks for result in self]:
return True
# Fall back to traditional access
return list.__contains__(self, item)
[docs] def to_list(self, discard_cosmetic_attributes=True):
"""
Render this ParameterList to a normal list, serializing each ParameterType object in it to dict.
Parameters
----------
discard_cosmetic_attributes : bool, optional. Default = True
Whether to discard non-spec attributes of each ParameterType object.
Returns
-------
parameter_list : List[dict]
A serialized representation of a ParameterList, with each ParameterType it contains converted to dict.
"""
parameter_list = list()
for parameter in self:
parameter_dict = parameter.to_dict(discard_cosmetic_attributes=discard_cosmetic_attributes)
parameter_list.append(parameter_dict)
return parameter_list
# TODO: Rename to better reflect role as parameter base class?
[docs]class ParameterType(_ParameterAttributeInitializer):
"""
Base class for SMIRNOFF parameter types.
This base class provides utilities to create new parameter types. See
the below for examples of how to do this.
.. warning :: This API is experimental and subject to change.
Attributes
----------
smirks : str
The SMIRKS pattern that this parameter matches.
id : str or None
An optional identifier for the parameter.
parent_id : str or None
Optionally, the identifier of the parameter of which this parameter
is a specialization.
See Also
--------
ParameterAttribute
IndexedParameterAttribute
Examples
--------
This class allows to define new parameter types by just listing its
attributes. In the example below, ``_VALENCE_TYPE`` AND ``_ELEMENT_NAME``
are used for the validation of the SMIRKS pattern associated to the
parameter and the automatic serialization/deserialization into a ``dict``.
>>> class MyBondParameter(ParameterType):
... _VALENCE_TYPE = 'Bond'
... _ELEMENT_NAME = 'Bond'
... length = ParameterAttribute(unit=unit.angstrom)
... k = ParameterAttribute(unit=unit.kilocalorie_per_mole / unit.angstrom**2)
...
The parameter automatically inherits the required smirks attribute
from ``ParameterType``. Associating a ``unit`` to a ``ParameterAttribute``
cause the attribute to accept only values in compatible units and to
parse string expressions.
>>> my_par = MyBondParameter(
... smirks='[*:1]-[*:2]',
... length='1.01 * angstrom',
... k=5 * unit.kilocalorie_per_mole / unit.angstrom**2
... )
>>> my_par.length
Quantity(value=1.01, unit=angstrom)
>>> my_par.k = 3.0 * unit.gram
Traceback (most recent call last):
...
openforcefield.utils.utils.IncompatibleUnitError: k=3.0 g should have units of kilocalorie/(angstrom**2*mole)
Each attribute can be made optional by specifying a default value,
and you can attach a converter function by passing a callable as an
argument or through the decorator syntax.
>>> class MyParameterType(ParameterType):
... _VALENCE_TYPE = 'Atom'
... _ELEMENT_NAME = 'Atom'
...
... attr_optional = ParameterAttribute(default=2)
... attr_all_to_float = ParameterAttribute(converter=float)
... attr_int_to_float = ParameterAttribute()
...
... @attr_int_to_float.converter
... def attr_int_to_float(self, attr, value):
... # This converter converts only integers to floats
... # and raise an exception for the other types.
... if isinstance(value, int):
... return float(value)
... elif not isinstance(value, float):
... raise TypeError(f"Cannot convert '{value}' to float")
... return value
...
>>> my_par = MyParameterType(smirks='[*:1]', attr_all_to_float='3.0', attr_int_to_float=1)
>>> my_par.attr_optional
2
>>> my_par.attr_all_to_float
3.0
>>> my_par.attr_int_to_float
1.0
The float() function can convert strings to integers, but our custom
converter forbids it
>>> my_par.attr_all_to_float = '2.0'
>>> my_par.attr_int_to_float = '4.0'
Traceback (most recent call last):
...
TypeError: Cannot convert '4.0' to float
Parameter attributes that can be indexed can be handled with the
``IndexedParameterAttribute``. These support unit validation and
converters exactly as ``ParameterAttribute``s, but the validation/conversion
is performed for each indexed attribute.
>>> class MyTorsionType(ParameterType):
... _VALENCE_TYPE = 'ProperTorsion'
... _ELEMENT_NAME = 'Proper'
... periodicity = IndexedParameterAttribute(converter=int)
... k = IndexedParameterAttribute(unit=unit.kilocalorie_per_mole)
...
>>> my_par = MyTorsionType(
... smirks='[*:1]-[*:2]-[*:3]-[*:4]',
... periodicity1=2,
... k1=5 * unit.kilocalorie_per_mole,
... periodicity2='3',
... k2=6 * unit.kilocalorie_per_mole,
... )
>>> my_par.periodicity
[2, 3]
"""
# ChemicalEnvironment valence type string expected by SMARTS string for this Handler
_VALENCE_TYPE = None
# The string mapping to this ParameterType in a SMIRNOFF data source
_ELEMENT_NAME = None
# Parameter attributes shared among all parameter types.
smirks = ParameterAttribute()
id = ParameterAttribute(default=None)
parent_id = ParameterAttribute(default=None)
@smirks.converter
def smirks(self, attr, smirks):
# Validate the SMIRKS string to ensure it matches the expected
# parameter type, raising an exception if it is invalid or doesn't
# tag a valid set of atoms.
# TODO: Make better switch using toolkit registry after refactoring ChemicalEnvironment module.
from openforcefield.utils.toolkits import OPENEYE_AVAILABLE, RDKIT_AVAILABLE
toolkit = None
if OPENEYE_AVAILABLE:
toolkit = 'openeye'
elif RDKIT_AVAILABLE:
toolkit = 'rdkit'
if toolkit is None:
raise ToolkitUnavailableException(
"Validating SMIRKS required either the OpenEye Toolkit or the RDKit."
" Unable to find either.")
# TODO: Add check to make sure we can't make tree non-hierarchical
# This would require parameter type knows which ParameterList it belongs to
ChemicalEnvironment.validate(
smirks, ensure_valence_type=self._VALENCE_TYPE, toolkit=toolkit)
return smirks
[docs] def __init__(self, smirks, allow_cosmetic_attributes=False, **kwargs):
"""
Create a ParameterType.
Parameters
----------
smirks : str
The SMIRKS match for the provided parameter type.
allow_cosmetic_attributes : bool optional. Default = False
Whether to permit non-spec kwargs ("cosmetic attributes"). If True, non-spec kwargs will be stored as
an attribute of this parameter which can be accessed and written out. Otherwise an exception will
be raised.
"""
# This is just to make smirks a required positional argument.
kwargs['smirks'] = smirks
super().__init__(allow_cosmetic_attributes=allow_cosmetic_attributes, **kwargs)
def __repr__(self):
ret_str = '<{} with '.format(self.__class__.__name__)
for attr, val in self.to_dict().items():
ret_str += f'{attr}: {val} '
ret_str += '>'
return ret_str
#======================================================================
# PARAMETER HANDLERS
#
# The following classes are Handlers that know how to create Force
# subclasses and add them to a System that is being created. Each Handler
# class must define three methods:
# 1) a constructor which takes as input hierarchical dictionaries of data
# conformant to the SMIRNOFF spec;
# 2) a create_force() method that constructs the Force object and adds it
# to the System; and
# 3) a labelForce() method that provides access to which terms are applied
# to which atoms in specified mols.
#======================================================================
# TODO: Should we have a parameter handler registry?
[docs]class ParameterHandler(_ParameterAttributeInitializer):
"""Base class for parameter handlers.
Parameter handlers are configured with some global parameters for a
given section. They may also contain a :class:`ParameterList` populated
with :class:`ParameterType` objects if they are responsible for assigning
SMIRKS-based parameters.
.. warning
Parameter handler objects can only belong to a single :class:`ForceField` object.
If you need to create a copy to attach to a different :class:`ForceField` object,
use ``create_copy()``.
.. warning :: This API is experimental and subject to change.
"""
_TAGNAME = None # str of section type handled by this ParameterHandler (XML element name for SMIRNOFF XML representation)
_INFOTYPE = None # container class with type information that will be stored in self._parameters
_OPENMMTYPE = None # OpenMM Force class (or None if no equivalent)
_DEPENDENCIES = None # list of ParameterHandler classes that must precede this, or None
_KWARGS = [] # Kwargs to catch when create_force is called
_SMIRNOFF_VERSION_INTRODUCED = 0.0 # the earliest version of SMIRNOFF spec that supports this ParameterHandler
_SMIRNOFF_VERSION_DEPRECATED = None # if deprecated, the first SMIRNOFF version number it is no longer used
_MIN_SUPPORTED_SECTION_VERSION = 0.3
_MAX_SUPPORTED_SECTION_VERSION = 0.3
version = ParameterAttribute()
@version.converter
def version(self, attr, new_version):
"""
Raise a parsing exception if the given section version is unsupported.
Raises
------
SMIRNOFFVersionError if an incompatible version is passed in.
"""
import packaging.version
from openforcefield.typing.engines.smirnoff import SMIRNOFFVersionError
# Use PEP-440 compliant version number comparison, if requested
if (
packaging.version.parse(str(new_version)) >
packaging.version.parse(str(self._MAX_SUPPORTED_SECTION_VERSION))
) or (
packaging.version.parse(str(new_version)) <
packaging.version.parse(str(self._MIN_SUPPORTED_SECTION_VERSION))
):
raise SMIRNOFFVersionError(
f'SMIRNOFF offxml file was written with version {new_version}, but this version '
f'of ForceField only supports version {self._MIN_SUPPORTED_SECTION_VERSION} '
f'to version {self._MAX_SUPPORTED_SECTION_VERSION}'
)
return new_version
[docs] def __init__(self, allow_cosmetic_attributes=False, skip_version_check=False, **kwargs):
"""
Initialize a ParameterHandler, optionally with a list of parameters and other kwargs.
Parameters
----------
allow_cosmetic_attributes : bool, optional. Default = False
Whether to permit non-spec kwargs. If True, non-spec kwargs will be stored as attributes of this object
and can be accessed and modified. Otherwise an exception will be raised if a non-spec kwarg is encountered.
skip_version_check: bool, optional. Default = False
If False, the SMIRNOFF section version will not be checked, and the ParameterHandler will be initialized
with version set to _MAX_SUPPORTED_SECTION_VERSION.
**kwargs : dict
The dict representation of the SMIRNOFF data source
"""
# Skip version check if requested.
if 'version' not in kwargs:
if skip_version_check:
kwargs['version'] = self._MAX_SUPPORTED_SECTION_VERSION
else:
raise SMIRNOFFSpecError(
f"Missing version while trying to construct {self.__class__}. "
f"0.3 SMIRNOFF spec requires each parameter section to have its own version."
)
# List of ParameterType objects (also behaves like an OrderedDict where keys are SMARTS).
self._parameters = ParameterList()
# Initialize ParameterAttributes and cosmetic attributes.
super().__init__(allow_cosmetic_attributes=allow_cosmetic_attributes, **kwargs)
def _add_parameters(self, section_dict, allow_cosmetic_attributes=False):
"""
Extend the ParameterList in this ParameterHandler using a SMIRNOFF data source.
Parameters
----------
section_dict : dict
The dict representation of a SMIRNOFF data source containing parameters to att to this ParameterHandler
allow_cosmetic_attributes : bool, optional. Default = False
Whether to allow non-spec fields in section_dict. If True, non-spec kwargs will be stored as an
attribute of the parameter. If False, non-spec kwargs will raise an exception.
"""
unitless_kwargs, attached_units = extract_serialized_units_from_dict(section_dict)
smirnoff_data = attach_units(unitless_kwargs, attached_units)
element_name = None
if self._INFOTYPE is not None:
element_name = self._INFOTYPE._ELEMENT_NAME
for key, val in smirnoff_data.items():
# Skip sections that aren't the parameter list
if key != element_name:
continue
# If there are multiple parameters, this will be a list. If there's just one, make it a list
if not (isinstance(val, list)):
val = [val]
# If we're reading the parameter list, iterate through and attach units to
# each parameter_dict, then use it to initialize a ParameterType
for unitless_param_dict in val:
param_dict = attach_units(unitless_param_dict, attached_units)
new_parameter = self._INFOTYPE(**param_dict,
allow_cosmetic_attributes=allow_cosmetic_attributes)
self._parameters.append(new_parameter)
@property
def parameters(self):
"""The ParameterList that holds this ParameterHandler's parameter objects"""
return self._parameters
# TODO: Do we need to return these, or can we handle this internally
@property
def known_kwargs(self):
"""List of kwargs that can be parsed by the function.
"""
# TODO: Should we use introspection to inspect the function signature instead?
return set(self._KWARGS)
[docs] def check_handler_compatibility(self, handler_kwargs):
"""
Checks if a set of kwargs used to create a ParameterHandler are compatible with this ParameterHandler. This is
called if a second handler is attempted to be initialized for the same tag.
Parameters
----------
handler_kwargs : dict
The kwargs that would be used to construct
Raises
------
IncompatibleParameterError if handler_kwargs are incompatible with existing parameters.
"""
pass
# TODO: Can we ensure SMIRKS and other parameters remain valid after manipulation?
[docs] def add_parameter(self, parameter_kwargs):
"""Add a parameter to the forcefield, ensuring all parameters are valid.
Parameters
----------
parameter_kwargs : dict
The kwargs to pass to the ParameterHandler.INFOTYPE (a ParameterType) constructor
"""
# TODO: Do we need to check for incompatibility with existing parameters?
new_parameter = self._INFOTYPE(**parameter_kwargs)
self._parameters.append(new_parameter)
[docs] def get_parameter(self, parameter_attrs):
"""
Return the parameters in this ParameterHandler that match the parameter_attrs argument
Parameters
----------
parameter_attrs : dict of {attr: value}
The attrs mapped to desired values (for example {"smirks": "[*:1]~[#16:2]=,:[#6:3]~[*:4]", "id": "t105"} )
Returns
-------
list of ParameterType objects
A list of matching ParameterType objects
"""
# TODO: This is a necessary API point for Lee-Ping's ForceBalance
pass
class _Match:
"""Represents a ParameterType which has been matched to
a given chemical environment.
"""
@property
def parameter_type(self):
"""ParameterType: The matched parameter type."""
return self._parameter_type
@property
def environment_match(self):
"""Topology._ChemicalEnvironmentMatch: The environment which matched the type."""
return self._environment_match
def __init__(self, parameter_type, environment_match):
"""Constructs a new ParameterHandlerMatch object.
Parameters
----------
parameter_type: ParameterType
The matched parameter type.
environment_match: Topology._ChemicalEnvironmentMatch
The environment which matched the type.
"""
self._parameter_type = parameter_type
self._environment_match = environment_match
[docs] def find_matches(self, entity):
"""Find the elements of the topology/molecule matched by a parameter type.
Parameters
----------
entity : openforcefield.topology.Topology
Topology to search.
Returns
---------
matches : ValenceDict[Tuple[int], ParameterHandler._Match]
``matches[particle_indices]`` is the ``ParameterType`` object
matching the tuple of particle indices in ``entity``.
"""
# TODO: Right now, this method is only ever called with an entity that is a Topoogy.
# Should we reduce its scope and have a check here to make sure entity is a Topology?
return self._find_matches(entity)
def _find_matches(self, entity, transformed_dict_cls=ValenceDict):
"""Implement find_matches() and allow using a difference valence dictionary.
Parameters
----------
entity : openforcefield.topology.Topology
Topology to search.
transformed_dict_cls: class
The type of dictionary to store the matches in. This
will determine how groups of atom indices are stored
and accessed (e.g for angles indices should be 0-1-2
and not 2-1-0).
Returns
---------
matches : `transformed_dict_cls` of ParameterHandlerMatch
``matches[particle_indices]`` is the ``ParameterType`` object
matching the tuple of particle indices in ``entity``.
"""
logger.debug('Finding matches for {}'.format(self.__class__.__name__))
matches = transformed_dict_cls()
# TODO: There are probably performance gains to be had here
# by performing this loop in reverse order, and breaking early once
# all environments have been matched.
for parameter_type in self._parameters:
matches_for_this_type = {}
for environment_match in entity.chemical_environment_matches(parameter_type.smirks):
# Update the matches for this parameter type.
handler_match = self._Match(parameter_type, environment_match)
matches_for_this_type[environment_match.topology_atom_indices] = handler_match
# Update matches of all parameter types.
matches.update(matches_for_this_type)
logger.debug('{:64} : {:8} matches'.format(
parameter_type.smirks, len(matches_for_this_type)))
logger.debug('{} matches identified'.format(len(matches)))
return matches
@staticmethod
def _assert_correct_connectivity(match, expected_connectivity=None):
"""A more performant version of the `topology.assert_bonded` method
to ensure that the results of `_find_matches` are valid.
Raises
------
ValueError
Raise an exception when the atoms in the match don't have
the correct connectivity.
Parameters
----------
match: ParameterHandler._Match
The match found by `_find_matches`
connectivity: list of tuple of int, optional
The expected connectivity of the match (e.g. for a torsion
expected_connectivity=[(0, 1), (1, 2), (2, 3)]). If `None`,
a connectivity of [(0, 1), ... (n - 1, n)] is assumed.
"""
# I'm not 100% sure this is really necessary... but this should do
# the same checks as the more costly assert_bonded method in the
# ParameterHandler.create_force methods.
if expected_connectivity is None:
expected_connectivity = [(i, i + 1) for i in range(len(match.environment_match.topology_atom_indices) - 1)]
reference_molecule = match.environment_match.reference_molecule
for connectivity in expected_connectivity:
atom_i = match.environment_match.reference_atom_indices[connectivity[0]]
atom_j = match.environment_match.reference_atom_indices[connectivity[1]]
reference_molecule.get_bond_between(atom_i, atom_j)
[docs] def assign_parameters(self, topology, system):
"""Assign parameters for the given Topology to the specified System object.
Parameters
----------
topology : openforcefield.topology.Topology
The Topology for which parameters are to be assigned.
Either a new Force will be created or parameters will be appended to an existing Force.
system : simtk.openmm.System
The OpenMM System object to add the Force (or append new parameters) to.
"""
pass
[docs] def postprocess_system(self, topology, system, **kwargs):
"""Allow the force to perform a a final post-processing pass on the System following parameter assignment, if needed.
Parameters
----------
topology : openforcefield.topology.Topology
The Topology for which parameters are to be assigned.
Either a new Force will be created or parameters will be appended to an existing Force.
system : simtk.openmm.System
The OpenMM System object to add the Force (or append new parameters) to.
"""
pass
[docs] def to_dict(self, discard_cosmetic_attributes=False):
"""
Convert this ParameterHandler to an OrderedDict, compliant with the SMIRNOFF data spec.
Parameters
----------
discard_cosmetic_attributes : bool, optional. Default = False.
Whether to discard non-spec parameter and header attributes in this ParameterHandler.
Returns
-------
smirnoff_data : OrderedDict
SMIRNOFF-spec compliant representation of this ParameterHandler and its internal ParameterList.
"""
smirnoff_data = OrderedDict()
# Populate parameter list
parameter_list = self._parameters.to_list(discard_cosmetic_attributes=discard_cosmetic_attributes)
# NOTE: This assumes that a ParameterHandler will have just one homogenous ParameterList under it
if self._INFOTYPE is not None:
#smirnoff_data[self._INFOTYPE._ELEMENT_NAME] = unitless_parameter_list
smirnoff_data[self._INFOTYPE._ELEMENT_NAME] = parameter_list
# Collect parameter and cosmetic attributes.
header_attribute_dict = super().to_dict(discard_cosmetic_attributes=discard_cosmetic_attributes)
smirnoff_data.update(header_attribute_dict)
return smirnoff_data
# -------------------------------
# Utilities for children classes.
# -------------------------------
@classmethod
def _check_all_valence_terms_assigned(cls, assigned_terms, valence_terms,
exception_cls=UnassignedValenceParameterException):
"""Check that all valence terms have been assigned and print a user-friendly error message.
Parameters
----------
assigned_terms : ValenceDict
Atom index tuples defining added valence terms.
valence_terms : Iterable[TopologyAtom] or Iterable[Iterable[TopologyAtom]]
Atom or atom tuples defining topological valence terms.
exception_cls : UnassignedValenceParameterException
A specific exception class to raise to allow catching only specific
types of errors.
"""
# Provided there are no duplicates in either list,
# or something weird like a bond has been added to
# a torsions list - this should work just fine I think.
# If we expect either of those assumptions to be incorrect,
# (i.e len(not_found_terms) > 0) we have bigger issues
# in the code and should be catching those cases elsewhere!
# The fact that we graph match all topol molecules to ref
# molecules should avoid the len(not_found_terms) > 0 case.
if len(assigned_terms) == len(valence_terms):
return
# Convert the valence term to a valence dictionary to make sure
# the order of atom indices doesn't matter for comparison.
valence_terms_dict = assigned_terms.__class__()
for atoms in valence_terms:
try:
# valence_terms is a list of TopologyAtom tuples.
atom_indices = (a.topology_particle_index for a in atoms)
except TypeError:
# valence_terms is a list of TopologyAtom.
atom_indices = (atoms.topology_particle_index,)
valence_terms_dict[atom_indices] = atoms
# Check that both valence dictionaries have the same keys (i.e. terms).
assigned_terms_set = set(assigned_terms.keys())
valence_terms_set = set(valence_terms_dict.keys())
unassigned_terms = valence_terms_set.difference(assigned_terms_set)
not_found_terms = assigned_terms_set.difference(valence_terms_set)
# Raise an error if there are unassigned terms.
err_msg = ""
if len(unassigned_terms) > 0:
unassigned_str = '\n- '.join([str(x) for x in unassigned_terms])
err_msg += ("{parameter_handler} was not able to find parameters for the following valence terms:\n"
"- {unassigned_str}").format(parameter_handler=cls.__name__,
unassigned_str=unassigned_str)
if len(not_found_terms) > 0:
if err_msg != "":
err_msg += '\n'
not_found_str = '\n- '.join([str(x) for x in not_found_terms])
err_msg += ("{parameter_handler} assigned terms that were not found in the topology:\n"
"- {not_found_str}").format(parameter_handler=cls.__name__,
not_found_str=not_found_str)
if err_msg != "":
err_msg += '\n'
raise exception_cls(err_msg)
def _check_attributes_are_equal(self, other, identical_attrs=(),
tolerance_attrs=(), tolerance=1e-6):
"""Utility function to check that the given attributes of the two handlers are equal.
Parameters
----------
identical_attrs : List[str]
Names of the parameters that must be checked with the equality operator.
tolerance_attrs : List[str]
Names of the parameters that must be equal up to a tolerance.
tolerance : float
The absolute tolerance used to compare the parameters.
"""
def get_unitless_values(attr):
this_val = getattr(self, attr)
other_val = getattr(other, attr)
# Strip quantities of their units before comparison.
try:
u = this_val.unit
except AttributeError:
return this_val, other_val
return this_val / u, other_val / u
for attr in identical_attrs:
this_val, other_val = get_unitless_values(attr)
if this_val != other_val:
raise IncompatibleParameterError(
"{} values are not identical. "
"(handler value: {}, incompatible value: {}".format(
attr, this_val, other_val))
for attr in tolerance_attrs:
this_val, other_val = get_unitless_values(attr)
if abs(this_val - other_val) > tolerance:
raise IncompatibleParameterError(
"Difference between '{}' values is beyond allowed tolerance {}. "
"(handler value: {}, incompatible value: {}".format(
attr, tolerance, this_val, other_val))
#=============================================================================================
class ConstraintHandler(ParameterHandler):
"""Handle SMIRNOFF ``<Constraints>`` tags
``ConstraintHandler`` must be applied before ``BondHandler`` and ``AngleHandler``,
since those classes add constraints for which equilibrium geometries are needed from those tags.
.. warning :: This API is experimental and subject to change.
"""
class ConstraintType(ParameterType):
"""A SMIRNOFF constraint type
.. warning :: This API is experimental and subject to change.
"""
_VALENCE_TYPE = 'Bond'
_ELEMENT_NAME = 'Constraint'
distance = ParameterAttribute(default=None, unit=unit.angstrom)
_TAGNAME = 'Constraints'
_INFOTYPE = ConstraintType
_OPENMMTYPE = None # don't create a corresponding OpenMM Force class
def create_force(self, system, topology, **kwargs):
constraint_matches = self.find_matches(topology)
for (atoms, constraint_match) in constraint_matches.items():
# Update constrained atom pairs in topology
#topology.add_constraint(*atoms, constraint.distance)
# If a distance is specified (constraint.distance != True), add the constraint here.
# Otherwise, the equilibrium bond length will be used to constrain the atoms in HarmonicBondHandler
constraint = constraint_match.parameter_type
if constraint.distance is None:
topology.add_constraint(*atoms, True)
else:
system.addConstraint(*atoms, constraint.distance)
topology.add_constraint(*atoms, constraint.distance)
#=============================================================================================
[docs]class BondHandler(ParameterHandler):
"""Handle SMIRNOFF ``<Bonds>`` tags
.. warning :: This API is experimental and subject to change.
"""
[docs] class BondType(ParameterType):
"""A SMIRNOFF bond type
.. warning :: This API is experimental and subject to change.
"""
# ChemicalEnvironment valence type string expected by SMARTS string for this Handler
_VALENCE_TYPE = 'Bond'
_ELEMENT_NAME = 'Bond'
# These attributes may be indexed (by integer bond order) if fractional bond orders are used.
length = ParameterAttribute(unit=unit.angstrom)
k = ParameterAttribute(unit=unit.kilocalorie_per_mole / unit.angstrom**2)
_TAGNAME = 'Bonds' # SMIRNOFF tag name to process
_INFOTYPE = BondType # class to hold force type info
_OPENMMTYPE = openmm.HarmonicBondForce # OpenMM force class to create
_DEPENDENCIES = [ConstraintHandler] # ConstraintHandler must be executed first
potential = ParameterAttribute(default='harmonic')
fractional_bondorder_method = ParameterAttribute(default=None)
fractional_bondorder_interpolation = ParameterAttribute(default='linear')
[docs] def check_handler_compatibility(self,
other_handler):
"""
Checks whether this ParameterHandler encodes compatible physics as another ParameterHandler. This is
called if a second handler is attempted to be initialized for the same tag.
Parameters
----------
other_handler : a ParameterHandler object
The handler to compare to.
Raises
------
IncompatibleParameterError if handler_kwargs are incompatible with existing parameters.
"""
string_attrs_to_compare = ['potential', 'fractional_bondorder_method', 'fractional_bondorder_interpolation']
self._check_attributes_are_equal(other_handler, identical_attrs=string_attrs_to_compare)
def create_force(self, system, topology, **kwargs):
# Create or retrieve existing OpenMM Force object
# TODO: The commented line below should replace the system.getForce search
#force = super(BondHandler, self).create_force(system, topology, **kwargs)
existing = [system.getForce(i) for i in range(system.getNumForces())]
existing = [f for f in existing if type(f) == self._OPENMMTYPE]
if len(existing) == 0:
force = self._OPENMMTYPE()
system.addForce(force)
else:
force = existing[0]
# Add all bonds to the system.
bond_matches = self.find_matches(topology)
skipped_constrained_bonds = 0 # keep track of how many bonds were constrained (and hence skipped)
for (topology_atom_indices, bond_match) in bond_matches.items():
# Get corresponding particle indices in Topology
#particle_indices = tuple([ atom.particle_index for atom in atoms ])
# Ensure atoms are actually bonded correct pattern in Topology
self._assert_correct_connectivity(bond_match)
# topology.assert_bonded(atoms[0], atoms[1])
bond_params = bond_match.parameter_type
match = bond_match.environment_match
# Compute equilibrium bond length and spring constant.
bond = match.reference_molecule.get_bond_between(*match.reference_atom_indices)
if bond.fractional_bond_order is None:
[k, length] = [bond_params.k, bond_params.length]
else:
# Interpolate using fractional bond orders
# TODO: Do we really want to allow per-bond specification of interpolation schemes?
order = bond.fractional_bond_order
if self.fractional_bondorder_interpolation == 'interpolate-linear':
k = bond_params.k[0] + (bond_params.k[1] - bond_params.k[0]) * (order - 1.)
length = bond_params.length[0] + (
bond_params.length[1] - bond_params.length[0]) * (order - 1.)
else:
raise Exception(
"Partial bondorder treatment {} is not implemented.".
format(self.fractional_bondorder_method))
is_constrained = topology.is_constrained(*topology_atom_indices)
# Handle constraints.
if is_constrained:
# Atom pair is constrained; we don't need to add a bond term.
skipped_constrained_bonds += 1
# Check if we need to add the constraint here to the equilibrium bond length.
if is_constrained is True:
# Mark that we have now assigned a specific constraint distance to this constraint.
topology.add_constraint(*topology_atom_indices, length)
# Add the constraint to the System.
system.addConstraint(*topology_atom_indices, length)
#system.addConstraint(*particle_indices, length)
continue
# Add harmonic bond to HarmonicBondForce
force.addBond(*topology_atom_indices, length, k)
logger.info('{} bonds added ({} skipped due to constraints)'.format(
len(bond_matches) - skipped_constrained_bonds, skipped_constrained_bonds))
# Check that no topological bonds are missing force parameters.
valence_terms = [list(b.atoms) for b in topology.topology_bonds]
self._check_all_valence_terms_assigned(assigned_terms=bond_matches, valence_terms=valence_terms,
exception_cls=UnassignedBondParameterException)
#=============================================================================================
[docs]class AngleHandler(ParameterHandler):
"""Handle SMIRNOFF ``<AngleForce>`` tags
.. warning :: This API is experimental and subject to change.
"""
[docs] class AngleType(ParameterType):
"""A SMIRNOFF angle type.
.. warning :: This API is experimental and subject to change.
"""
_VALENCE_TYPE = 'Angle' # ChemicalEnvironment valence type string expected by SMARTS string for this Handler
_ELEMENT_NAME = 'Angle'
angle = ParameterAttribute(unit=unit.degree)
k = ParameterAttribute(unit=unit.kilocalorie_per_mole / unit.degree**2)
_TAGNAME = 'Angles' # SMIRNOFF tag name to process
_INFOTYPE = AngleType # class to hold force type info
_OPENMMTYPE = openmm.HarmonicAngleForce # OpenMM force class to create
_DEPENDENCIES = [ConstraintHandler] # ConstraintHandler must be executed first
potential = ParameterAttribute(default='harmonic')
[docs] def check_handler_compatibility(self,
other_handler):
"""
Checks whether this ParameterHandler encodes compatible physics as another ParameterHandler. This is
called if a second handler is attempted to be initialized for the same tag.
Parameters
----------
other_handler : a ParameterHandler object
The handler to compare to.
Raises
------
IncompatibleParameterError if handler_kwargs are incompatible with existing parameters.
"""
string_attrs_to_compare = ['potential']
self._check_attributes_are_equal(other_handler, identical_attrs=string_attrs_to_compare)
def create_force(self, system, topology, **kwargs):
#force = super(AngleHandler, self).create_force(system, topology, **kwargs)
existing = [system.getForce(i) for i in range(system.getNumForces())]
existing = [f for f in existing if type(f) == self._OPENMMTYPE]
if len(existing) == 0:
force = self._OPENMMTYPE()
system.addForce(force)
else:
force = existing[0]
# Add all angles to the system.
angle_matches = self.find_matches(topology)
skipped_constrained_angles = 0 # keep track of how many angles were constrained (and hence skipped)
for (atoms, angle_match) in angle_matches.items():
# Ensure atoms are actually bonded correct pattern in Topology
# for (i, j) in [(0, 1), (1, 2)]:
# topology.assert_bonded(atoms[i], atoms[j])
self._assert_correct_connectivity(angle_match)
if topology.is_constrained(
atoms[0], atoms[1]) and topology.is_constrained(
atoms[1], atoms[2]) and topology.is_constrained(
atoms[0], atoms[2]):
# Angle is constrained; we don't need to add an angle term.
skipped_constrained_angles += 1
continue
angle = angle_match.parameter_type
force.addAngle(*atoms, angle.angle, angle.k)
logger.info('{} angles added ({} skipped due to constraints)'.format(
len(angle_matches) - skipped_constrained_angles,
skipped_constrained_angles))
# Check that no topological angles are missing force parameters
self._check_all_valence_terms_assigned(assigned_terms=angle_matches,
valence_terms=list(topology.angles),
exception_cls=UnassignedAngleParameterException)
#=============================================================================================
# TODO: This is technically a validator, not a converter, but ParameterAttribute doesn't support them yet (it'll be easy if we switch to use the attrs library).
def _allow_only(allowed_values):
"""A converter that checks the new value is only in a set.
"""
allowed_values = frozenset(allowed_values)
def _value_checker(instance, attr, new_value):
if new_value not in allowed_values:
err_msg = (f'Attempted to set {instance.__class__.__name__}.{attr.name} '
f'to {new_value}. Currently, only the following values '
f'are supported: {sorted(allowed_values)}.')
raise SMIRNOFFSpecError(err_msg)
return new_value
return _value_checker
# TODO: There's a lot of duplicated code in ProperTorsionHandler and ImproperTorsionHandler
[docs]class ProperTorsionHandler(ParameterHandler):
"""Handle SMIRNOFF ``<ProperTorsionForce>`` tags
.. warning :: This API is experimental and subject to change.
"""
[docs] class ProperTorsionType(ParameterType):
"""A SMIRNOFF torsion type for proper torsions.
.. warning :: This API is experimental and subject to change.
"""
_VALENCE_TYPE = 'ProperTorsion'
_ELEMENT_NAME = 'Proper'
periodicity = IndexedParameterAttribute(converter=int)
phase = IndexedParameterAttribute(unit=unit.degree)
k = IndexedParameterAttribute(unit=unit.kilocalorie_per_mole)
idivf = IndexedParameterAttribute(default=None, converter=float)
_TAGNAME = 'ProperTorsions' # SMIRNOFF tag name to process
_INFOTYPE = ProperTorsionType # info type to store
_OPENMMTYPE = openmm.PeriodicTorsionForce # OpenMM force class to create
potential = ParameterAttribute(
default='k*(1+cos(periodicity*theta-phase))',
converter=_allow_only(['k*(1+cos(periodicity*theta-phase))'])
)
default_idivf = ParameterAttribute(default='auto')
[docs] def check_handler_compatibility(self,
other_handler):
"""
Checks whether this ParameterHandler encodes compatible physics as another ParameterHandler. This is
called if a second handler is attempted to be initialized for the same tag.
Parameters
----------
other_handler : a ParameterHandler object
The handler to compare to.
Raises
------
IncompatibleParameterError if handler_kwargs are incompatible with existing parameters.
"""
float_attrs_to_compare = []
string_attrs_to_compare = ['potential']
if self.default_idivf == 'auto':
string_attrs_to_compare.append('default_idivf')
else:
float_attrs_to_compare.append('default_idivf')
self._check_attributes_are_equal(other_handler, identical_attrs=string_attrs_to_compare,
tolerance_attrs=float_attrs_to_compare)
def create_force(self, system, topology, **kwargs):
#force = super(ProperTorsionHandler, self).create_force(system, topology, **kwargs)
existing = [system.getForce(i) for i in range(system.getNumForces())]
existing = [f for f in existing if type(f) == self._OPENMMTYPE]
if len(existing) == 0:
force = self._OPENMMTYPE()
system.addForce(force)
else:
force = existing[0]
# Add all proper torsions to the system.
torsion_matches = self.find_matches(topology)
for (atom_indices, torsion_match) in torsion_matches.items():
# Ensure atoms are actually bonded correct pattern in Topology
self._assert_correct_connectivity(torsion_match)
torsion = torsion_match.parameter_type
for (periodicity, phase, k, idivf) in zip(torsion.periodicity,
torsion.phase, torsion.k, torsion.idivf):
if idivf == 'auto':
# TODO: Implement correct "auto" behavior
raise NotImplementedError("The OpenForceField toolkit hasn't implemented "
"support for the torsion `idivf` value of 'auto'")
force.addTorsion(atom_indices[0], atom_indices[1],
atom_indices[2], atom_indices[3], periodicity,
phase, k/idivf)
logger.info('{} torsions added'.format(len(torsion_matches)))
# Check that no topological torsions are missing force parameters
# I can see the apeal of these kind of methods as an 'absolute' check
# that things have gone well, but I think just making sure that the
# reference molecule has been fully parametrised should have the same
# effect! It would be good to eventually refactor things so that everything
# is focused on the single unique molecules, and then simply just cloned
# onto the system. It seems like John's proposed System object would do
# exactly this.
self._check_all_valence_terms_assigned(assigned_terms=torsion_matches,
valence_terms=list(topology.propers),
exception_cls=UnassignedProperTorsionParameterException)
# TODO: There's a lot of duplicated code in ProperTorsionHandler and ImproperTorsionHandler
[docs]class ImproperTorsionHandler(ParameterHandler):
"""Handle SMIRNOFF ``<ImproperTorsionForce>`` tags
.. warning :: This API is experimental and subject to change.
"""
[docs] class ImproperTorsionType(ParameterType):
"""A SMIRNOFF torsion type for improper torsions.
.. warning :: This API is experimental and subject to change.
"""
_VALENCE_TYPE = 'ImproperTorsion'
_ELEMENT_NAME = 'Improper'
periodicity = IndexedParameterAttribute(converter=int)
phase = IndexedParameterAttribute(unit=unit.degree)
k = IndexedParameterAttribute(unit=unit.kilocalorie_per_mole)
idivf = IndexedParameterAttribute(default=None, converter=float)
_TAGNAME = 'ImproperTorsions' # SMIRNOFF tag name to process
_INFOTYPE = ImproperTorsionType # info type to store
_OPENMMTYPE = openmm.PeriodicTorsionForce # OpenMM force class to create
potential = ParameterAttribute(
default='k*(1+cos(periodicity*theta-phase))',
converter=_allow_only(['k*(1+cos(periodicity*theta-phase))'])
)
default_idivf = ParameterAttribute(default='auto')
[docs] def check_handler_compatibility(self,
other_handler):
"""
Checks whether this ParameterHandler encodes compatible physics as another ParameterHandler. This is
called if a second handler is attempted to be initialized for the same tag.
Parameters
----------
other_handler : a ParameterHandler object
The handler to compare to.
Raises
------
IncompatibleParameterError if handler_kwargs are incompatible with existing parameters.
"""
float_attrs_to_compare = []
string_attrs_to_compare = ['potential']
if self.default_idivf == 'auto':
string_attrs_to_compare.append('default_idivf')
else:
float_attrs_to_compare.append('default_idivf')
self._check_attributes_are_equal(other_handler, identical_attrs=string_attrs_to_compare,
tolerance_attrs=float_attrs_to_compare)
[docs] def find_matches(self, entity):
"""Find the improper torsions in the topology/molecule matched by a parameter type.
Parameters
----------
entity : openforcefield.topology.Topology
Topology to search.
Returns
---------
matches : ImproperDict[Tuple[int], ParameterHandler._Match]
``matches[atom_indices]`` is the ``ParameterType`` object
matching the 4-tuple of atom indices in ``entity``.
"""
return self._find_matches(entity, transformed_dict_cls=ImproperDict)
def create_force(self, system, topology, **kwargs):
#force = super(ImproperTorsionHandler, self).create_force(system, topology, **kwargs)
#force = super().create_force(system, topology, **kwargs)
existing = [system.getForce(i) for i in range(system.getNumForces())]
existing = [
f for f in existing if type(f) == openmm.PeriodicTorsionForce
]
if len(existing) == 0:
force = openmm.PeriodicTorsionForce()
system.addForce(force)
else:
force = existing[0]
# Add all improper torsions to the system
improper_matches = self.find_matches(topology)
for (atom_indices, improper_match) in improper_matches.items():
# Ensure atoms are actually bonded correct pattern in Topology
# For impropers, central atom is atom 1
# for (i, j) in [(0, 1), (1, 2), (1, 3)]:
# topology.assert_bonded(atom_indices[i], atom_indices[j])
self._assert_correct_connectivity(improper_match, [(0, 1), (1, 2), (1, 3)])
improper = improper_match.parameter_type
# TODO: This is a lazy hack. idivf should be set according to the ParameterHandler's default_idivf attrib
if improper.idivf is None:
improper.idivf = [3 for item in improper.k]
# Impropers are applied in three paths around the trefoil having the same handedness
for (improper_periodicity, improper_phase, improper_k, improper_idivf) in zip(improper.periodicity,
improper.phase, improper.k, improper.idivf):
# TODO: Implement correct "auto" behavior
if improper_idivf == 'auto':
improper_idivf = 3
logger.warning("The OpenForceField toolkit hasn't implemented "
"support for the torsion `idivf` value of 'auto'."
"Currently assuming a value of '3' for impropers.")
# Permute non-central atoms
others = [atom_indices[0], atom_indices[2], atom_indices[3]]
# ((0, 1, 2), (1, 2, 0), and (2, 0, 1)) are the three paths around the trefoil
for p in [(others[i], others[j], others[k]) for (i, j, k) in [(0, 1, 2), (1, 2, 0), (2, 0, 1)]]:
# The torsion force gets added three times, since the k is divided by three
force.addTorsion(atom_indices[1], p[0], p[1], p[2],
improper_periodicity, improper_phase, improper_k/improper_idivf)
logger.info(
'{} impropers added, each applied in a six-fold trefoil'.format(
len(improper_matches)))
[docs]class vdWHandler(ParameterHandler):
"""Handle SMIRNOFF ``<vdW>`` tags
.. warning :: This API is experimental and subject to change.
"""
[docs] class vdWType(ParameterType):
"""A SMIRNOFF vdWForce type.
.. warning :: This API is experimental and subject to change.
"""
_VALENCE_TYPE = 'Atom' # ChemicalEnvironment valence type expected for SMARTS
_ELEMENT_NAME = 'Atom'
epsilon = ParameterAttribute(unit=unit.kilocalorie_per_mole)
sigma = ParameterAttribute(default=None, unit=unit.angstrom)
rmin_half = ParameterAttribute(default=None, unit=unit.angstrom)
def __init__(self, **kwargs):
sigma = kwargs.get('sigma', None)
rmin_half = kwargs.get('rmin_half', None)
if (sigma is None) and (rmin_half is None):
raise SMIRNOFFSpecError("Either sigma or rmin_half must be specified.")
if (sigma is not None) and (rmin_half is not None):
raise SMIRNOFFSpecError(
"BOTH sigma and rmin_half cannot be specified simultaneously."
)
super().__init__(**kwargs)
_TAGNAME = 'vdW' # SMIRNOFF tag name to process
_INFOTYPE = vdWType # info type to store
_OPENMMTYPE = openmm.NonbondedForce # OpenMM force class to create
# _KWARGS = ['ewaldErrorTolerance',
# 'useDispersionCorrection',
# 'usePbc'] # Kwargs to catch when create_force is called
potential = ParameterAttribute(
default='Lennard-Jones-12-6',
converter=_allow_only(['Lennard-Jones-12-6'])
)
combining_rules = ParameterAttribute(
default='Lorentz-Berthelot',
converter=_allow_only(['Lorentz-Berthelot'])
)
scale12 = ParameterAttribute(default=0.0, converter=float)
scale13 = ParameterAttribute(default=0.0, converter=float)
scale14 = ParameterAttribute(default=0.5, converter=float)
scale15 = ParameterAttribute(default=1.0, converter=float)
cutoff = ParameterAttribute(default=9.0 * unit.angstroms, unit=unit.angstrom)
switch_width = ParameterAttribute(default=1.0 * unit.angstroms, unit=unit.angstrom)
method = ParameterAttribute(
default='cutoff',
converter=_allow_only(['cutoff', 'PME'])
)
# TODO: Use _allow_only when ParameterAttribute will support multiple converters (it'll be easy when we switch to use the attrs library)
@scale12.converter
def scale12(self, attrs, new_scale12):
if new_scale12 != 0.0:
raise SMIRNOFFSpecError("Current OFF toolkit is unable to handle scale12 values other than 0.0. "
"Specified 1-2 scaling was {}".format(self._scale12))
return new_scale12
@scale13.converter
def scale13(self, attrs, new_scale13):
if new_scale13 != 0.0:
raise SMIRNOFFSpecError("Current OFF toolkit is unable to handle scale13 values other than 0.0. "
"Specified 1-3 scaling was {}".format(self._scale13))
return new_scale13
@scale15.converter
def scale15(self, attrs, new_scale15):
if new_scale15 != 1.0:
raise SMIRNOFFSpecError("Current OFF toolkit is unable to handle scale15 values other than 1.0. "
"Specified 1-5 scaling was {}".format(self._scale15))
return new_scale15
# Tolerance when comparing float attributes for handler compatibility.
_SCALETOL = 1e-5
[docs] def check_handler_compatibility(self,
other_handler):
"""
Checks whether this ParameterHandler encodes compatible physics as another ParameterHandler. This is
called if a second handler is attempted to be initialized for the same tag.
Parameters
----------
other_handler : a ParameterHandler object
The handler to compare to.
Raises
------
IncompatibleParameterError if handler_kwargs are incompatible with existing parameters.
"""
float_attrs_to_compare = ['scale12', 'scale13', 'scale14', 'scale15']
string_attrs_to_compare = ['potential', 'combining_rules', 'method']
unit_attrs_to_compare = ['cutoff']
self._check_attributes_are_equal(other_handler, identical_attrs=string_attrs_to_compare,
tolerance_attrs=float_attrs_to_compare+unit_attrs_to_compare,
tolerance=self._SCALETOL)
def create_force(self, system, topology, **kwargs):
force = openmm.NonbondedForce()
# If we're using PME, then the only possible openMM Nonbonded type is LJPME
if self._method == 'PME':
# If we're given a nonperiodic box, we always set NoCutoff. Later we'll add support for CutoffNonPeriodic
if (topology.box_vectors is None):
force.setNonbondedMethod(openmm.NonbondedForce.NoCutoff)
# if (topology.box_vectors is None):
# raise SMIRNOFFSpecError("If vdW method is PME, a periodic Topology "
# "must be provided")
else:
force.setNonbondedMethod(openmm.NonbondedForce.LJPME)
force.setCutoffDistance(9. * unit.angstrom)
force.setEwaldErrorTolerance(1.e-4)
# If method is cutoff, then we currently support openMM's PME for periodic system and NoCutoff for nonperiodic
elif self._method == 'cutoff':
# If we're given a nonperiodic box, we always set NoCutoff. Later we'll add support for CutoffNonPeriodic
if (topology.box_vectors is None):
force.setNonbondedMethod(openmm.NonbondedForce.NoCutoff)
else:
force.setNonbondedMethod(openmm.NonbondedForce.PME)
force.setUseDispersionCorrection(True)
force.setCutoffDistance(self._cutoff)
system.addForce(force)
# Iterate over all defined Lennard-Jones types, allowing later matches to override earlier ones.
atom_matches = self.find_matches(topology)
# Create all particles.
for _ in topology.topology_particles:
force.addParticle(0.0, 1.0, 0.0)
# Set the particle Lennard-Jones terms.
for atom_key, atom_match in atom_matches.items():
atom_idx = atom_key[0]
ljtype = atom_match.parameter_type
if ljtype.sigma is None:
sigma = 2. * ljtype.rmin_half / (2.**(1. / 6.))
else:
sigma = ljtype.sigma
force.setParticleParameters(atom_idx, 0.0, sigma,
ljtype.epsilon)
# Check that no atoms (n.b. not particles) are missing force parameters.
self._check_all_valence_terms_assigned(assigned_terms=atom_matches,
valence_terms=list(topology.topology_atoms))
# TODO: Can we express separate constraints for postprocessing and normal processing?
[docs] def postprocess_system(self, system, topology, **kwargs):
# Create exceptions based on bonds.
# TODO: This postprocessing must occur after the ChargeIncrementModelHandler
# QUESTION: Will we want to do this for *all* cases, or would we ever want flexibility here?
bond_particle_indices = []
for topology_molecule in topology.topology_molecules:
top_mol_particle_start_index = topology_molecule.atom_start_topology_index
for topology_bond in topology_molecule.bonds:
top_index_1 = topology_molecule._ref_to_top_index[topology_bond.bond.atom1_index]
top_index_2 = topology_molecule._ref_to_top_index[topology_bond.bond.atom2_index]
top_index_1 += top_mol_particle_start_index
top_index_2 += top_mol_particle_start_index
bond_particle_indices.append((top_index_1, top_index_2))
for force in system.getForces():
# TODO: Should we just store which `Force` object we are adding to and use that instead,
# to prevent interference with other kinds of forces in the future?
# TODO: Can we generalize this to allow for `CustomNonbondedForce` implementations too?
if isinstance(force, openmm.NonbondedForce):
#nonbonded.createExceptionsFromBonds(bond_particle_indices, self.coulomb14scale, self.lj14scale)
# TODO: Don't mess with electrostatic scaling here. Have a separate electrostatics handler.
force.createExceptionsFromBonds(bond_particle_indices, 0.83333,
self._scale14)
#force.createExceptionsFromBonds(bond_particle_indices, self.coulomb14scale, self._scale14)
[docs]class ElectrostaticsHandler(ParameterHandler):
"""Handles SMIRNOFF ``<Electrostatics>`` tags.
.. warning :: This API is experimental and subject to change.
"""
_TAGNAME = 'Electrostatics'
_OPENMMTYPE = openmm.NonbondedForce
_DEPENDENCIES = [vdWHandler]
scale12 = ParameterAttribute(default=0.0, converter=float)
scale13 = ParameterAttribute(default=0.0, converter=float)
scale14 = ParameterAttribute(default=0.833333, converter=float)
scale15 = ParameterAttribute(default=1.0, converter=float)
cutoff = ParameterAttribute(default=9.0 * unit.angstrom, unit=unit.angstrom)
switch_width = ParameterAttribute(default=0.0 * unit.angstrom, unit=unit.angstrom)
method = ParameterAttribute(
default='PME',
converter=_allow_only(['Coulomb', 'PME'])
)
# TODO: Use _allow_only when ParameterAttribute will support multiple converters (it'll be easy when we switch to use the attrs library)
@scale12.converter
def scale12(self, attrs, new_scale12):
if new_scale12 != 0.0:
raise SMIRNOFFSpecError("Current OFF toolkit is unable to handle scale12 values other than 0.0. "
"Specified 1-2 scaling was {}".format(self._scale12))
return new_scale12
@scale13.converter
def scale13(self, attrs, new_scale13):
if new_scale13 != 0.0:
raise SMIRNOFFSpecError("Current OFF toolkit is unable to handle scale13 values other than 0.0. "
"Specified 1-3 scaling was {}".format(self._scale13))
return new_scale13
@scale15.converter
def scale15(self, attrs, new_scale15):
if new_scale15 != 1.0:
raise SMIRNOFFSpecError("Current OFF toolkit is unable to handle scale15 values other than 1.0. "
"Specified 1-5 scaling was {}".format(self._scale15))
return new_scale15
@switch_width.converter
def switch_width(self, attr, new_switch_width):
if self._switch_width != 0.0 * unit.angstrom:
raise IncompatibleParameterError(
"The current implementation of the Open Force Field toolkit can not "
"support an electrostatic switching width. Currently only `0.0 angstroms` "
f"is supported (SMIRNOFF data specified {new_switch_width})"
)
# Tolerance when comparing float attributes for handler compatibility.
_SCALETOL = 1e-5
[docs] def check_handler_compatibility(self,
other_handler):
"""
Checks whether this ParameterHandler encodes compatible physics as another ParameterHandler. This is
called if a second handler is attempted to be initialized for the same tag.
Parameters
----------
other_handler : a ParameterHandler object
The handler to compare to.
Raises
------
IncompatibleParameterError if handler_kwargs are incompatible with existing parameters.
"""
float_attrs_to_compare = ['scale12', 'scale13', 'scale14', 'scale15']
string_attrs_to_compare = ['method']
unit_attrs_to_compare = ['cutoff', 'switch_width']
self._check_attributes_are_equal(other_handler, identical_attrs=string_attrs_to_compare,
tolerance_attrs=float_attrs_to_compare+unit_attrs_to_compare,
tolerance=self._SCALETOL)
def create_force(self, system, topology, **kwargs):
existing = [system.getForce(i) for i in range(system.getNumForces())]
existing = [
f for f in existing if type(f) == openmm.NonbondedForce
]
force = existing[0]
# Set the nonbonded method
settings_matched = False
current_nb_method = force.getNonbondedMethod()
# First, check whether the vdWHandler set the nonbonded method to LJPME, because that means
# that electrostatics also has to be PME
if (current_nb_method == openmm.NonbondedForce.LJPME) and (self._method != 'PME'):
raise IncompatibleParameterError("In current Open Force Field toolkit implementation, if vdW "
"treatment is set to LJPME, electrostatics must also be PME "
"(electrostatics treatment currently set to {}".format(self._method))
# Then, set nonbonded methods based on method keyword
if self._method == 'PME':
# Check whether the topology is nonperiodic, in which case we always switch to NoCutoff
# (vdWHandler will have already set this to NoCutoff)
# TODO: This is an assumption right now, and a bad one. See issue #219
if topology.box_vectors is None:
assert current_nb_method == openmm.NonbondedForce.NoCutoff
settings_matched = True
# raise IncompatibleParameterError("Electrostatics handler received PME method keyword, but a nonperiodic"
# " topology. Use of PME electrostatics requires a periodic topology.")
else:
if current_nb_method == openmm.NonbondedForce.LJPME:
pass
# There's no need to check for matching cutoff/tolerance here since both are hard-coded defaults
else:
force.setNonbondedMethod(openmm.NonbondedForce.PME)
force.setCutoffDistance(9. * unit.angstrom)
force.setEwaldErrorTolerance(1.e-4)
settings_matched = True
# If vdWHandler set the nonbonded method to NoCutoff, then we don't need to change anything
elif self._method == 'Coulomb':
if topology.box_vectors is None:
# (vdWHandler will have already set this to NoCutoff)
assert current_nb_method == openmm.NonbondedForce.NoCutoff
settings_matched = True
else:
raise IncompatibleParameterError("Electrostatics method set to Coulomb, and topology is periodic. "
"In the future, this will lead to use of OpenMM's CutoffPeriodic "
"Nonbonded force method, however this is not supported in the "
"current Open Force Field toolkit.")
# If the vdWHandler set the nonbonded method to PME, then ensure that it has the same cutoff
elif self._method == 'reaction-field':
if topology.box_vectors is None:
# (vdWHandler will have already set this to NoCutoff)
assert current_nb_method == openmm.NonbondedForce.NoCutoff
settings_matched = True
else:
raise IncompatibleParameterError("Electrostatics method set to reaction-field. In the future, "
"this will lead to use of OpenMM's CutoffPeriodic or CutoffNonPeriodic"
" Nonbonded force method, however this is not supported in the "
"current Open Force Field toolkit")
if not settings_matched:
raise IncompatibleParameterError("Unable to support provided vdW method, electrostatics "
"method ({}), and topology periodicity ({}) selections. Additional "
"options for nonbonded treatment may be added in future versions "
"of the Open Force Field toolkit.".format(self._method,
topology.box_vectors is not None))
# TODO: Calculate exceptions
class ChargeIncrementModelHandler(ParameterHandler):
"""Handle SMIRNOFF ``<ChargeIncrementModel>`` tags
.. warning :: This API is experimental and subject to change.
"""
class ChargeIncrementType(ParameterType):
"""A SMIRNOFF bond charge correction type.
.. warning :: This API is experimental and subject to change.
"""
_VALENCE_TYPE = 'Bond' # ChemicalEnvironment valence type expected for SMARTS
_ELEMENT_NAME = 'ChargeIncrement'
chargeincrement = IndexedParameterAttribute(unit=unit.elementary_charge)
_TAGNAME = 'ChargeIncrementModel' # SMIRNOFF tag name to process
_INFOTYPE = ChargeIncrementType # info type to store
_OPENMMTYPE = openmm.NonbondedForce # OpenMM force class to create or utilize
# TODO: The structure of this is still undecided
_KWARGS = ['charge_from_molecules']
number_of_conformers = ParameterAttribute(default=10, converter=int)
quantum_chemical_method = ParameterAttribute(
default='AM1',
converter=_allow_only(['AM1'])
)
partial_charge_method = ParameterAttribute(
default='CM2',
converter=_allow_only(['CM2'])
)
def __init__(self, **kwargs):
raise NotImplementedError("ChangeIncrementHandler is not yet implemented, pending finalization of the "
"SMIRNOFF spec")
def check_handler_compatibility(self,
other_handler,
assume_missing_is_default=True):
"""
Checks whether this ParameterHandler encodes compatible physics as another ParameterHandler. This is
called if a second handler is attempted to be initialized for the same tag.
Parameters
----------
other_handler : a ParameterHandler object
The handler to compare to.
Raises
------
IncompatibleParameterError if handler_kwargs are incompatible with existing parameters.
"""
int_attrs_to_compare = ['number_of_conformers']
string_attrs_to_compare = ['quantum_chemical_method', 'partial_charge_method']
self._check_attributes_are_equal(other_handler,
identical_attrs=string_attrs_to_compare+int_attrs_to_compare)
def assign_charge_from_molecules(self, molecule, charge_mols):
"""
Given an input molecule, checks against a list of molecules for an isomorphic match. If found, assigns
partial charges from the match to the input molecule.
Parameters
----------
molecule : an openforcefield.topology.FrozenMolecule
The molecule to have partial charges assigned if a match is found.
charge_mols : list of [openforcefield.topology.FrozenMolecule]
A list of molecules with charges already assigned.
Returns
-------
match_found : bool
Whether a match was found. If True, the input molecule will have been modified in-place.
"""
from networkx.algorithms.isomorphism import GraphMatcher
# Define the node/edge attributes that we will use to match the atoms/bonds during molecule comparison
node_match_func = lambda x, y: ((x['atomic_number'] == y['atomic_number']) and
(x['stereochemistry'] == y['stereochemistry']) and
(x['is_aromatic'] == y['is_aromatic'])
)
edge_match_func = lambda x, y: ((x['order'] == y['order']) and
(x['stereochemistry'] == y['stereochemistry']) and
(x['is_aromatic'] == y['is_aromatic'])
)
# Check each charge_mol for whether it's isomorphic to the input molecule
for charge_mol in charge_mols:
if molecule.is_isomorphic(charge_mol):
# Take the first valid atom indexing map
ref_mol_G = molecule.to_networkx()
charge_mol_G = charge_mol.to_networkX()
GM = GraphMatcher(
charge_mol_G,
ref_mol_G,
node_match=node_match_func,
edge_match=edge_match_func)
for mapping in GM.isomorphisms_iter():
topology_atom_map = mapping
break
# Set the partial charges
charge_mol_charges = charge_mol.get_partial_charges()
temp_mol_charges = charge_mol_charges.copy()
for charge_idx, ref_idx in topology_atom_map:
temp_mol_charges[ref_idx] = charge_mol_charges[charge_idx]
molecule.set_partial_charges(temp_mol_charges)
return True
# If no match was found, return False
return False
def create_force(self, system, topology, **kwargs):
from openforcefield.topology import FrozenMolecule, TopologyAtom, TopologyVirtualSite
existing = [system.getForce(i) for i in range(system.getNumForces())]
existing = [f for f in existing if type(f) == self._OPENMMTYPE]
if len(existing) == 0:
force = self._OPENMMTYPE()
system.addForce(force)
else:
force = existing[0]
for ref_mol in topology.reference_molecules:
# Make a temporary copy of ref_mol to assign charges from charge_mol
temp_mol = FrozenMolecule(ref_mol)
# First, check whether any of the reference molecules in the topology are in the charge_from_mol list
charges_from_charge_mol = False
if 'charge_from_mol' in kwargs:
charges_from_charge_mol = self.assign_charge_from_molecules(temp_mol, kwargs['charge_from_mol'])
# If the molecule wasn't assigned parameters from a manually-input charge_mol, calculate them here
if not(charges_from_charge_mol):
temp_mol.generate_conformers(n_conformers=10)
temp_mol.compute_partial_charges(quantum_chemical_method=self._quantum_chemical_method,
partial_charge_method=self._partial_charge_method)
# Assign charges to relevant atoms
for topology_molecule in topology._reference_molecule_to_topology_molecules[ref_mol]:
for topology_particle in topology_molecule.particles:
topology_particle_index = topology_particle.topology_particle_index
if type(topology_particle) is TopologyAtom:
ref_mol_particle_index = topology_particle.atom.molecule_particle_index
if type(topology_particle) is TopologyVirtualSite:
ref_mol_particle_index = topology_particle.virtual_site.molecule_particle_index
particle_charge = temp_mol._partial_charges[ref_mol_particle_index]
# Retrieve nonbonded parameters for reference atom (charge not set yet)
_, sigma, epsilon = force.getParticleParameters(topology_particle_index)
# Set the nonbonded force with the partial charge
force.setParticleParameters(topology_particle_index,
particle_charge, sigma,
epsilon)
# TODO: Move chargeModel and library residue charges to SMIRNOFF spec
def postprocess_system(self, system, topology, **kwargs):
bond_matches = self.find_matches(topology)
# Apply bond charge increments to all appropriate force groups
# QUESTION: Should we instead apply this to the Topology in a preprocessing step, prior to spreading out charge onto virtual sites?
for force in system.getForces():
if force.__class__.__name__ in [
'NonbondedForce'
]: # TODO: We need to apply this to all Force types that involve charges, such as (Custom)GBSA forces and CustomNonbondedForce
for (atoms, bond_match) in bond_matches.items():
bond = bond_match.parameter_type
# Get corresponding particle indices in Topology
particle_indices = tuple(
[atom.particle_index for atom in atoms])
# Retrieve parameters
[charge0, sigma0, epsilon0] = force.getParticleParameters(
particle_indices[0])
[charge1, sigma1, epsilon1] = force.getParticleParameters(
particle_indices[1])
# Apply bond charge increment
charge0 -= bond.increment
charge1 += bond.increment
# Update charges
force.setParticleParameters(particle_indices[0], charge0,
sigma0, epsilon0)
force.setParticleParameters(particle_indices[1], charge1,
sigma1, epsilon1)
# TODO: Calculate exceptions
class GBSAParameterHandler(ParameterHandler):
"""Handle SMIRNOFF ``<GBSAParameterHandler>`` tags
.. warning :: This API is experimental and subject to change.
"""
# TODO: Differentiate between global and per-particle parameters for each model.
# Global parameters for surface area (SA) component of model
SA_expected_parameters = {
'ACE': ['surface_area_penalty', 'solvent_radius'],
None: [],
}
# Per-particle parameters for generalized Born (GB) model
GB_expected_parameters = {
'HCT': ['radius', 'scale'],
'OBC1': ['radius', 'scale'],
'OBC2': ['radius', 'scale'],
}
class GBSAType(ParameterType):
"""A SMIRNOFF GBSA type.
.. warning :: This API is experimental and subject to change.
"""
_VALENCE_TYPE = 'Atom'
_ELEMENT_NAME = 'Atom' # TODO: This isn't actually in the spec
radius = ParameterAttribute(unit=unit.angstrom)
scale = ParameterAttribute(converter=float)
def __init__(self, **kwargs):
super().__init__(**kwargs)
# # Store model parameters.
# gb_model = parent.attrib['gb_model']
# expected_parameters = GBSAParameterHandler.GB_expected_parameters[
# gb_model]
# provided_parameters = list()
# missing_parameters = list()
# for name in expected_parameters:
# if name in node.attrib:
# provided_parameters.append(name)
# value = _extract_quantity_from_xml_element(
# node, parent, name)
# setattr(self, name, value)
# else:
# missing_parameters.append(name)
# if len(missing_parameters) > 0:
# msg = 'GBSAForce: missing per-atom parameters for tag %s' % str(
# node)
# msg += 'model "%s" requires specification of per-atom parameters %s\n' % (
# gb_model, str(expected_parameters))
# msg += 'provided parameters : %s\n' % str(provided_parameters)
# msg += 'missing parameters: %s' % str(missing_parameters)
# raise Exception(msg)
# TODO: Finish this
_TAGNAME = 'GBSA'
_INFOTYPE = GBSAType
#_OPENMMTYPE =
def __init__(self, **kwargs):
super().__init__(**kwargs)
# TODO: Fix this
def parseElement(self):
# Initialize GB model
gb_model = element.attrib['gb_model']
valid_GB_models = GBSAParameterHandler.GB_expected_parameters.keys()
if not gb_model in valid_GB_models:
raise Exception(
'Specified GBSAForce model "%s" not one of valid models: %s' %
(gb_model, valid_GB_models))
self.gb_model = gb_model
# Initialize SA model
sa_model = element.attrib['sa_model']
valid_SA_models = GBSAParameterHandler.SA_expected_parameters.keys()
if not sa_model in valid_SA_models:
raise Exception(
'Specified GBSAForce SA_model "%s" not one of valid models: %s'
% (sa_model, valid_SA_models))
self.sa_model = sa_model
# Store parameters for GB and SA models
# TODO: Deep copy?
self.parameters = element.attrib
# TODO: Generalize this to allow forces to know when their OpenMM Force objects can be combined
def checkCompatibility(self, Handler):
"""
Check compatibility of this Handler with another Handlers.
"""
Handler = existing[0]
if (Handler.gb_model != self.gb_model):
raise ValueError(
'Found multiple GBSAForce tags with different GB model specifications'
)
if (Handler.sa_model != self.sa_model):
raise ValueError(
'Found multiple GBSAForce tags with different SA model specifications'
)
# TODO: Check other attributes (parameters of GB and SA models) automatically?
def create_force(self, system, topology, **args):
# TODO: Rework this
from openforcefield.typing.engines.smirnoff import gbsaforces
force_class = getattr(gbsaforces, self.gb_model)
force = force_class(**self.parameters)
system.addForce(force)
# Add all GBSA terms to the system.
expected_parameters = GBSAParameterHandler.GB_expected_parameters[
self.gb_model]
# Create all particles with parameters set to zero
atoms = self.getMatches(topology)
nparams = 1 + len(expected_parameters) # charge + GBSA parameters
params = [0.0 for i in range(nparams)]
for _ in topology.topology_particles():
force.addParticle(params)
# Set the GBSA parameters (keeping charges at zero for now)
for (atoms, gbsa_type) in atoms.items():
atom = atoms[0]
# Set per-particle parameters for assigned parameters
params = [atom.charge] + [
getattr(gbsa_type, name) for name in expected_parameters
]
force.setParticleParameters(atom.particle_index, params)
if __name__ == '__main__':
import doctest
doctest.run_docstring_examples(ParameterType, globals())
Open Force Field Toolkit
0.4.1