Source code for qcelemental.models.procedures

from enum import Enum
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

try:
    from pydantic.v1 import Field, conlist, constr, validator
except ImportError:  # Will also trap ModuleNotFoundError
    from pydantic import Field, conlist, constr, validator

from ..util import provenance_stamp
from .basemodels import ProtoModel
from .common_models import (
    ComputeError,
    DriverEnum,
    Model,
    Provenance,
    qcschema_input_default,
    qcschema_optimization_input_default,
    qcschema_optimization_output_default,
    qcschema_torsion_drive_input_default,
    qcschema_torsion_drive_output_default,
)
from .molecule import Molecule
from .results import AtomicResult

if TYPE_CHECKING:
    try:
        from pydantic.v1.typing import ReprArgs
    except ImportError:  # Will also trap ModuleNotFoundError
        from pydantic.typing import ReprArgs


class TrajectoryProtocolEnum(str, Enum):
    """
    Which gradient evaluations to keep in an optimization trajectory.
    """

    all = "all"
    initial_and_final = "initial_and_final"
    final = "final"
    none = "none"


class OptimizationProtocols(ProtoModel):
    """
    Protocols regarding the manipulation of a Optimization output data.
    """

    trajectory: TrajectoryProtocolEnum = Field(
        TrajectoryProtocolEnum.all, description=str(TrajectoryProtocolEnum.__doc__)
    )

    class Config:
        force_skip_defaults = True


class QCInputSpecification(ProtoModel):
    """
    A compute description for energy, gradient, and Hessian computations used in a geometry optimization.
    """

    schema_name: constr(strip_whitespace=True, regex=qcschema_input_default) = qcschema_input_default  # type: ignore
    schema_version: int = 1

    driver: DriverEnum = Field(DriverEnum.gradient, description=str(DriverEnum.__doc__))
    model: Model = Field(..., description=str(Model.__doc__))
    keywords: Dict[str, Any] = Field({}, description="The program specific keywords to be used.")

    extras: Dict[str, Any] = Field(
        {},
        description="Additional information to bundle with the computation. Use for schema development and scratch space.",
    )


[docs]class OptimizationInput(ProtoModel): id: Optional[str] = None hash_index: Optional[str] = None schema_name: constr( # type: ignore strip_whitespace=True, regex=qcschema_optimization_input_default ) = qcschema_optimization_input_default schema_version: int = 1 keywords: Dict[str, Any] = Field({}, description="The optimization specific keywords to be used.") extras: Dict[str, Any] = Field({}, description="Extra fields that are not part of the schema.") protocols: OptimizationProtocols = Field(OptimizationProtocols(), description=str(OptimizationProtocols.__doc__)) input_specification: QCInputSpecification = Field(..., description=str(QCInputSpecification.__doc__)) initial_molecule: Molecule = Field(..., description="The starting molecule for the geometry optimization.") provenance: Provenance = Field(Provenance(**provenance_stamp(__name__)), description=str(Provenance.__doc__)) def __repr_args__(self) -> "ReprArgs": return [ ("model", self.input_specification.model.dict()), ("molecule_hash", self.initial_molecule.get_hash()[:7]), ]
[docs]class OptimizationResult(OptimizationInput): schema_name: constr( # type: ignore strip_whitespace=True, regex=qcschema_optimization_output_default ) = qcschema_optimization_output_default final_molecule: Optional[Molecule] = Field(..., description="The final molecule of the geometry optimization.") trajectory: List[AtomicResult] = Field( ..., description="A list of ordered Result objects for each step in the optimization." ) energies: List[float] = Field(..., description="A list of ordered energies for each step in the optimization.") stdout: Optional[str] = Field(None, description="The standard output of the program.") stderr: Optional[str] = Field(None, description="The standard error of the program.") success: bool = Field( ..., description="The success of a given programs execution. If False, other fields may be blank." ) error: Optional[ComputeError] = Field(None, description=str(ComputeError.__doc__)) provenance: Provenance = Field(..., description=str(Provenance.__doc__)) @validator("trajectory", each_item=False) def _trajectory_protocol(cls, v, values): # Do not propogate validation errors if "protocols" not in values: raise ValueError("Protocols was not properly formed.") keep_enum = values["protocols"].trajectory if keep_enum == "all": pass elif keep_enum == "initial_and_final": if len(v) != 2: v = [v[0], v[-1]] elif keep_enum == "final": if len(v) != 1: v = [v[-1]] elif keep_enum == "none": v = [] else: raise ValueError(f"Protocol `trajectory:{keep_enum}` is not understood.") return v
class OptimizationSpecification(ProtoModel): """ A specification for how a geometry optimization should be performed **inside** of another procedure. Notes ----- * This class is still provisional and may be subject to removal and re-design. """ schema_name: constr(strip_whitespace=True, regex="qcschema_optimization_specification") = "qcschema_optimization_specification" # type: ignore schema_version: int = 1 procedure: str = Field(..., description="Optimization procedure to run the optimization with.") keywords: Dict[str, Any] = Field({}, description="The optimization specific keywords to be used.") protocols: OptimizationProtocols = Field(OptimizationProtocols(), description=str(OptimizationProtocols.__doc__)) @validator("procedure") def _check_procedure(cls, v): return v.lower() class TDKeywords(ProtoModel): """ TorsionDriveRecord options Notes ----- * This class is still provisional and may be subject to removal and re-design. """ dihedrals: List[Tuple[int, int, int, int]] = Field( ..., description="The list of dihedrals to select for the TorsionDrive operation. Each entry is a tuple of integers " "of for particle indices.", ) grid_spacing: List[int] = Field( ..., description="List of grid spacing for dihedral scan in degrees. Multiple values will be mapped to each " "dihedral angle.", ) dihedral_ranges: Optional[List[Tuple[int, int]]] = Field( None, description="A list of dihedral range limits as a pair (lower, upper). " "Each range corresponds to the dihedrals in input.", ) energy_decrease_thresh: Optional[float] = Field( None, description="The threshold of the smallest energy decrease amount to trigger activating optimizations from " "grid point.", ) energy_upper_limit: Optional[float] = Field( None, description="The threshold if the energy of a grid point that is higher than the current global minimum, to " "start new optimizations, in unit of a.u. I.e. if energy_upper_limit = 0.05, current global " "minimum energy is -9.9 , then a new task starting with energy -9.8 will be skipped.", ) class TorsionDriveInput(ProtoModel): """Inputs for running a torsion drive. Notes ----- * This class is still provisional and may be subject to removal and re-design. """ schema_name: constr(strip_whitespace=True, regex=qcschema_torsion_drive_input_default) = qcschema_torsion_drive_input_default # type: ignore schema_version: int = 1 keywords: TDKeywords = Field(..., description="The torsion drive specific keywords to be used.") extras: Dict[str, Any] = Field({}, description="Extra fields that are not part of the schema.") input_specification: QCInputSpecification = Field(..., description=str(QCInputSpecification.__doc__)) initial_molecule: conlist(item_type=Molecule, min_items=1) = Field( ..., description="The starting molecule(s) for the torsion drive." ) optimization_spec: OptimizationSpecification = Field( ..., description="Settings to use for optimizations at each grid angle." ) provenance: Provenance = Field(Provenance(**provenance_stamp(__name__)), description=str(Provenance.__doc__)) @validator("input_specification") def _check_input_specification(cls, value): assert value.driver == DriverEnum.gradient, "driver must be set to gradient" return value class TorsionDriveResult(TorsionDriveInput): """Results from running a torsion drive. Notes ----- * This class is still provisional and may be subject to removal and re-design. """ schema_name: constr(strip_whitespace=True, regex=qcschema_torsion_drive_output_default) = qcschema_torsion_drive_output_default # type: ignore schema_version: int = 1 final_energies: Dict[str, float] = Field( ..., description="The final energy at each angle of the TorsionDrive scan." ) final_molecules: Dict[str, Molecule] = Field( ..., description="The final molecule at each angle of the TorsionDrive scan." ) optimization_history: Dict[str, List[OptimizationResult]] = Field( ..., description="The map of each angle of the TorsionDrive scan to each optimization computations.", ) stdout: Optional[str] = Field(None, description="The standard output of the program.") stderr: Optional[str] = Field(None, description="The standard error of the program.") success: bool = Field( ..., description="The success of a given programs execution. If False, other fields may be blank." ) error: Optional[ComputeError] = Field(None, description=str(ComputeError.__doc__)) provenance: Provenance = Field(..., description=str(Provenance.__doc__)) def Optimization(*args, **kwargs): """QC Optimization Results Schema. .. deprecated:: 0.12 Use :py:func:`qcelemental.models.OptimizationResult` instead. """ from warnings import warn warn( "Optimization has been renamed to OptimizationResult and will be removed as soon as v0.13.0", DeprecationWarning ) return OptimizationResult(*args, **kwargs)