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potential.py
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potential.py
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""" A move to make Potentials as universal as possible
"""
import json
import hashlib
import yaml
import numpy as np
import pymatgen as pmg
from .schema import PotentialSchema
from .parameter import FloatParameter
from . import db
class Potential:
def __init__(self, schema):
schema_load, errors = PotentialSchema().load(schema)
self.schema = schema_load
self._apply_constraints()
self._collect_parameters()
def _apply_constraints(self):
for constraint, value in self.schema['spec'].get('constraint', {}).items():
if constraint == 'charge_balance':
composition = pmg.core.Composition(value)
charges = self.schema['spec'].get('charge', {})
if not {e.symbol for e in composition.keys()} <= charges.keys():
raise ValueError('charge ballance constrains requires all elements to be defined in charge')
for charge_element in sorted(charges):
parameter = charges[charge_element]
if isinstance(parameter, FloatParameter) and not parameter.fixed:
break
else:
if abs(sum(float(charges[element.symbol]) * amount for element, amount in composition.items())) > 1e-8:
raise ValueError('no parameters to apply charge constraint and charge does ballance')
continue
parameter.computed = lambda: -sum(float(charges[element.symbol]) * amount for element, amount in composition.items() if element.symbol != charge_element)
def _collect_parameters(self):
self._parameters = []
def _walk(value): # Ordered traversal of dictionary
if isinstance(value, dict):
for key in sorted(value.keys()):
_walk(value[key])
elif isinstance(value, (tuple, list)):
for item in value:
_walk(item)
elif isinstance(value, FloatParameter):
self._parameters.append(value)
_walk(self.schema)
self._optimization_parameters = []
self._optimization_parameter_indicies = []
for i, p in enumerate(self._parameters):
if not p.fixed:
self._optimization_parameters.append(p)
self._optimization_parameter_indicies.append(i)
@classmethod
def from_file(cls, filename, format=None):
if format not in {'json', 'yaml'}:
if filename.endswith('json'):
format = 'json'
elif filename.endswith('yaml') or filename.endswith('yml'):
format = 'yaml'
else:
raise ValueError('unrecognized filetype from filename %s' % filename)
if format == 'json':
with open(filename) as f:
return cls(json.load(f))
elif format in {'yaml', 'yml'}:
with open(filename) as f:
return cls(yaml.load(f))
@classmethod
def from_run_evaluation(cls, schema, initial_parameters, optimization_indicies, optimization_parameters, optimization_bounds):
parameters = [value for value in initial_parameters]
for i, value, bounds in zip(optimization_indicies, optimization_parameters, optimization_bounds):
parameters[i] = {'initial': value, 'bounds': bounds}
index = 0
def _walk(value): # Ordered traversal of dictionary
nonlocal index
if isinstance(value, dict):
for key in sorted(value.keys()):
if isinstance(value[key], str) and value[key] == 'FloatParameter':
value[key] = parameters[index]
index += 1
else:
_walk(value[key])
elif isinstance(value, (list)):
for i, item in enumerate(value):
if isinstance(item, str) and item == 'FloatParameter':
value[i] = parameters[index]
index += 1
else:
_walk(item)
_walk(schema)
# Adding constraints
for constraint, value in schema['spec'].get('constraint', {}).items():
if constraint == 'charge_balance':
composition = pmg.core.Composition(value)
charges = schema['spec'].get('charge', {})
if not {e.symbol for e in composition.keys()} <= charges.keys():
raise ValueError('charge ballance constrains requires all elements to be defined in charge')
for charge_element in sorted(charges):
parameter = charges[charge_element]
if isinstance(parameter, (float, int)):
charge = 0
bounds = [0, 0]
for element, amount in composition.items():
if element.symbol != charge_element:
if isinstance(charges[element.symbol], dict):
charge -= charges[element.symbol]['initial'] * amount
bounds[0] -= charges[element.symbol].get('bounds', [0.0, 0.0])[1] * amount
bounds[1] -= charges[element.symbol].get('bounds', [0.0, 0.0])[0] * amount
else:
charge -= float(charges[element.symbol])
charges[charge_element] = {'initial': charge, 'bounds': bounds}
break
else:
raise ValueError('unable to apply charge constraint no fixed values')
return cls(schema)
def md5hash(self):
potential_str = json.dumps(self.as_dict(with_parameters=False), sort_keys=True)
return hashlib.md5(potential_str.encode('utf-8')).hexdigest()
@classmethod
def from_best_optimized_for_potential_calulations(cls, potential, calculations, database_filename=None):
# TODO: Notice that for now calculations are not considered for selection
potential_str = json.dumps(potential.as_dict(with_parameters=False), sort_keys=True)
potential_hash = hashlib.md5(potential_str.encode('utf-8')).hexdigest()
optimized_potential = potential.copy()
with db.DatabaseManager(database_filename or 'dftfit.db').transaction() as session:
evaluation = session.query(db.Evaluation) \
.filter(db.Evaluation.potential_id == potential_hash) \
.order_by(db.Evaluation.score).first()
parameters = json.loads(evaluation.parameters)
optimized_potential.optimization_parameters = parameters
return optimized_potential
def as_dict(self, with_parameters=True):
if with_parameters:
schema_dump, errors = PotentialSchema().dump(self.schema)
return schema_dump
else:
class CustomEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, FloatParameter):
return "FloatParameter"
else:
return super().default(obj)
return json.loads(json.dumps(self.schema, cls=CustomEncoder))
def write_file(self, filename):
with open(filename, 'w') as f:
f.write(str(self))
def __copy__(self):
return type(self)(self.as_dict())
def copy(self):
return self.__copy__()
def __hash__(self):
return hash(json.dumps(self.as_dict(with_parameters=False), sort_keys=True))
def __eq__(self, other):
return hash(self) == hash(other) and np.all(np.isclose(self.parameters, other.parameters, rtol=1e-16))
@property
def parameters(self):
""" Returns parameters for potentials as a list of float values
"""
return np.array([float(parameter) for parameter in self._parameters])
@property
def optimization_parameters(self):
return np.array([float(parameter) for parameter in self._optimization_parameters])
@optimization_parameters.setter
def optimization_parameters(self, parameters):
""" Update potential with given parameters
"""
if len(parameters) != len(self._optimization_parameters):
raise ValueError('updating parameters does not match length of potential parameters')
for parameter, update_parameter in zip(self._optimization_parameters, parameters):
parameter.current = float(update_parameter)
@property
def optimization_bounds(self):
return np.array([parameter.bounds for parameter in self._optimization_parameters])
@property
def optimization_parameter_indicies(self):
return np.array(self._optimization_parameter_indicies)
@property
def elements(self):
""" Return a set of the elements that potential applies to
"""
elements = set()
for element in self.schema['spec'].get('charge', {}):
elements.add(element)
for parameter in self.schema['spec'].get('pair', {}).get('parameters', []):
for element in parameter.get('elements', []):
elements.add(element)
return elements
def __repr__(self):
return self.__str__()
def __str__(self):
return json.dumps(self.as_dict(), sort_keys=True, indent=4)