CurationWorkflowSchema
- class openff.evaluator.datasets.curation.workflow.CurationWorkflowSchema(*, component_schemas: List[Union[ConvertExcessDensityDataSchema, FilterDuplicatesSchema, FilterByTemperatureSchema, FilterByPressureSchema, FilterByMoleFractionSchema, FilterByRacemicSchema, FilterByElementsSchema, FilterByPropertyTypesSchema, FilterByStereochemistrySchema, FilterByChargedSchema, FilterByIonicLiquidSchema, FilterBySmilesSchema, FilterBySmirksSchema, FilterByNComponentsSchema, FilterBySubstancesSchema, FilterByEnvironmentsSchema, ImportFreeSolvSchema, SelectSubstancesSchema, SelectDataPointsSchema, ImportThermoMLDataSchema]] = None)[source]
A schemas which encodes how a set of curation components should be applied sequentially to a data set.
- __init__(**data: Any) None
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
Methods
__init__(**data)Create a new model by parsing and validating input data from keyword arguments.
construct([_fields_set])Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
copy(*[, include, exclude, update, deep])Duplicate a model, optionally choose which fields to include, exclude and change.
dict(*[, include, exclude, by_alias, ...])Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
from_orm(obj)json(*[, include, exclude, by_alias, ...])Generate a JSON representation of the model, include and exclude arguments as per dict().
parse_file(path, *[, content_type, ...])parse_obj(obj)parse_raw(b, *[, content_type, encoding, ...])schema([by_alias, ref_template])schema_json(*[, by_alias, ref_template])update_forward_refs(**localns)Try to update ForwardRefs on fields based on this Model, globalns and localns.
validate(value)Attributes
component_schemas- classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) Model
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
- copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) Model
Duplicate a model, optionally choose which fields to include, exclude and change.
- Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model
- Returns
new model instance
- dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) DictStrAny
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
- json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) unicode
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().