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compute_wrappers.py
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compute_wrappers.py
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import json
import logging
from copy import deepcopy
import numpy as np
from qcelemental.models import AtomicInput, AtomicResult, Molecule
from qcelemental.util.serialization import json_dumps
from .exceptions import OptError
from . import log_name
logger = logging.getLogger(f"{log_name}{__name__}")
class ComputeWrapper:
"""An implementation of MolSSI's qc schema
Parameters
----------
JSON_dict : dict
should match qcschema_input format. version 1
"""
def __init__(self, molecule, model, keywords, program):
self.molecule = molecule
self.model = model
self.keywords = keywords
self.program = program
self.trajectory = []
self.energies = []
@classmethod
def init_full(cls, molecule, model, keywords, program, trajectory, energies):
wrapper = cls(molecule, model, keywords, program)
wrapper.trajectory = trajectory
wrapper.energies = energies
return wrapper
def update_geometry(self, geom: np.ndarray):
"""Updates EngineWrapper for requesting calculation
Parameters
----------
geom : np.ndarray
cartesian geometry 1D list
Returns
-------
json_for_input : dict
"""
self.molecule["geometry"] = [i for i in geom.flat]
def generate_schema_input(self, driver):
molecule = Molecule(**self.molecule)
inp = AtomicInput(molecule=molecule, model=self.model, keywords=self.keywords, driver=driver)
return inp
def generate_schema_input_for_procedure(self, driver):
molecule = Molecule(**self.molecule)
mbspec = self.keywords
mbspec["driver"] = driver
return {"molecule": molecule, "specification": mbspec}
def _compute(self, driver):
"""Abstract style method for child classes"""
pass
def compute(self, geom, driver, return_full=True, print_result=False):
"""Perform calculation of type driver
Parameters
----------
geom: np.ndarray
driver: str
return_full: boolean
print_result: boolean
Returns
-------
dict
"""
self.update_geometry(geom)
ret = self._compute(driver)
# Decodes the Result Schema to remove numpy elements (Makes ret JSON serializable)
ret = json.loads(json_dumps(ret))
self.trajectory.append(ret)
if print_result:
logger.debug(json.dumps(ret, indent=2))
if ret["success"]:
self.energies.append(ret["properties"]["return_energy"])
else:
raise OptError(
f"Error encountered for {driver} calc. {ret['error']['error_message']}",
ret["error"]["error_type"],
)
if return_full:
return ret
else:
return ret["return_result"]
def energy(self, return_full=False):
return self._compute("energy")
def gradient(self, return_full=False):
return self._compute("gradient")
def hessian(self, return_full=False):
return self._compute("hessian")
def make_computer_from_dict(computer_type, d):
mol = d.get("molecule")
mod = d.get("model")
key = d.get("keywords")
prog = d.get("program")
traj = d.get("trajectory")
ener = d.get("energies")
if computer_type == "psi4":
return Psi4Computer.init_full(mol, mod, key, prog, traj, ener)
elif computer_type == "qc":
return QCEngineComputer.init_full(mol, mod, key, prog, traj, ener)
elif computer_type == "user":
return UserComputer.init_full(mol, mod, key, prog, traj, ener)
else:
raise OptError("computer_type is unknown")
class Psi4Computer(ComputeWrapper):
def _compute(self, driver):
import psi4
inp = self.generate_schema_input(driver)
if "1.3" in psi4.__version__:
ret = psi4.json_wrapper.run_json_qcschema(inp.dict(), clean=True)
else:
ret = psi4.schema_wrapper.run_json_qcschema(inp.dict(), clean=True, json_serialization=True)
ret = AtomicResult(**ret)
return ret
class QCEngineComputer(ComputeWrapper):
def _compute(self, driver):
import qcengine
local_options = {}
if self.program == "psi4":
import psi4
local_options["memory"] = psi4.core.get_memory() / 1000000000
local_options["ncores"] = psi4.core.get_num_threads()
if self.model == "(proc_spec_in_options)":
logger.debug("QCEngineComputer.path: ManyBody")
inp = self.generate_schema_input_for_procedure(driver)
ret = qcengine.compute_procedure(inp, "qcmanybody", True, local_options)
else:
logger.debug("QCEngineComputer.path: Atomic")
inp = self.generate_schema_input(driver)
ret = qcengine.compute(inp, self.program, True, local_options)
return ret
# Class to produce a compliant output with user provided energy/gradient/hessian
class UserComputer(ComputeWrapper):
def __init__(self, molecule, model, keywords, program):
super().__init__(molecule, model, keywords, program)
self.external_energy = None
self.external_gradient = None
self.external_hessian = None
output_skeleton = {
"id": None,
"schema_name": "qcschema_output",
"schema_version": 1,
"model": {"method": "unknown", "basis": "unknown"},
"provenance": {"creator": "User", "version": "0.1"},
"properties": {},
"extras": {"qcvars": {}},
"stdout": "User provided energy, gradient, or hessian is returned",
"stderr": None,
"success": True,
"error": None,
}
def _compute(self, driver):
E = self.external_energy
gX = self.external_gradient
HX = self.external_hessian
if driver == "hessian":
if HX is None or gX is None or E is None:
raise OptError("Must provide hessian, gradient, and energy.")
elif driver == "gradient":
if gX is None or E is None:
raise OptError("Must provide gradient and energy.")
elif driver == "energy":
if E is None:
raise OptError("Must provide energy.")
result = deepcopy(UserComputer.output_skeleton)
result["driver"] = driver
mol = Molecule(**self.molecule)
result["molecule"] = mol
NRE = mol.nuclear_repulsion_energy()
result["properties"]["nuclear_repulsion_energy"] = NRE
result["extras"]["qcvars"]["NUCLEAR REPULSION ENERGY"] = NRE
result["properties"]["return_energy"] = E
result["extras"]["qcvars"]["CURRENT ENERGY"] = E
if driver in ["gradient", "hessian"]:
result["extras"]["qcvars"]["CURRENT GRADIENT"] = gX
if driver == "hessian":
result["extras"]["qcvars"]["CURRENT HESSIAN"] = HX
if driver == "energy":
result["return_result"] = E
elif driver == "gradient":
result["return_result"] = gX
elif driver == "hessian":
result["return_result"] = HX
return AtomicResult(**result)