TorsionDriveResultCollection

class openff.qcsubmit.results.TorsionDriveResultCollection(*, entries, provenance={}, type='TorsionDriveResultCollection')[source]

A class which stores a reference to, and allows the retrieval of, data from a single torsion drive result record stored in a QCFractal instance.

Parameters:

entries (Dict[str, List[TorsionDriveResult]]) –
provenance (Dict[str, Any]) –
type (Literal['TorsionDriveResultCollection']) –

__init__(**data)

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.

Parameters:: data (Any) –
Return type:: None

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.
`create_optimization_dataset`(dataset_name, ...)	Create an optimization dataset from the results of the current torsion drive dataset.
`dict`(*[, include, exclude, by_alias, ...])	Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
`filter`(*filters)	Filter this collection by applying a set of filters sequentially, returning a new collection containing only the retained entries.
`from_datasets`(datasets[, spec_name])	Retrieve the COMPLETE record ids referenced by the specified datasets.
`from_orm`(obj)
`from_server`(client, datasets[, spec_name])	Retrieve (and deduplicate) the COMPLETE record ids referenced by the specified datasets.
`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])
`smirnoff_coverage`(force_field[, verbose, ...])	Returns a summary of how many unique molecules within this collection would be assigned each of the parameters in a force field.
`to_records`()	Returns the native QCPortal record objects for each of the records referenced in this collection along with a corresponding OpenFF molecule object.
`update_forward_refs`(**localns)	Try to update ForwardRefs on fields based on this Model, globalns and localns.
`validate`(value)

Attributes

`n_molecules`	Returns the number of unique molecules referenced by this collection.
`n_results`	Returns the number of results in this collection.
`type`
`entries`
`provenance`

classmethod from_datasets(datasets, spec_name='default')[source]

Retrieve the COMPLETE record ids referenced by the specified datasets.

Parameters:

datasets (TorsiondriveDataset | Iterable[TorsiondriveDataset]) – The datasets to retrieve the records from.
spec_name (str) – The name of the QC specification that the records to retrieve should have been computed using.

Returns:

A results collection object containing the record ids and a minimal amount of associated metadata such as the CMILES of the associated molecule.

Return type:

TorsionDriveResultCollection

classmethod from_server(client, datasets, spec_name='default')[source]

Retrieve (and deduplicate) the COMPLETE record ids referenced by the specified datasets.

Parameters:

client (PortalClient) – The fractal client that should be used to interface with the running QCFractal instance which stores the datasets and their associated results records.
datasets (str | Iterable[str]) – The names of the datasets
spec_name (str) – The name of the QC specification that the records to retrieve should have been computed using.

Returns:

A results collection object containing the record ids and a minimal amount of associated metadata such as the CMILES of the associated molecule.

Return type:

TorsionDriveResultCollection

to_records()[source]

Returns the native QCPortal record objects for each of the records referenced in this collection along with a corresponding OpenFF molecule object.

Each molecule will contain the minimum energy conformer referenced by the record.

Return type:: List[Tuple[TorsiondriveRecord, Molecule]]

create_optimization_dataset(dataset_name, description, tagline, metadata=None)[source]

Create an optimization dataset from the results of the current torsion drive dataset. This will result in many constrained optimizations for each molecule.

Note

The final geometry of each torsiondrive constrained optimization is supplied as a starting geometry.

Parameters:

dataset_name (str) – The name that will be given to the new dataset.
tagline (str) – The tagline that should be given to the new dataset.
description (str) – The description that should be given to the new dataset.
metadata (Metadata | None) – The metadata for the new dataset.

Returns:

The created optimization dataset.

Return type:

OptimizationDataset

smirnoff_coverage(force_field, verbose=False, driven_only=False)[source]

Returns a summary of how many unique molecules within this collection would be assigned each of the parameters in a force field.

Notes

Parameters which would not be assigned to any molecules in the collection will not be included in the returned summary.

Parameters:

force_field (ForceField) – The force field containing the parameters to summarize.
verbose (bool) – If true a progress bar will be shown on screen.
driven_only (bool) –
Whether to only include parameters that are applied (at least partially) across a central torsion bond that was driven.

This option is still experimental and may in cases not count parameters that are actually applied or double count certain applied parameters.

Returns:

A dictionary of the form coverage[handler_name][parameter_smirks] = count which stores the number of unique molecules within this collection that would be assigned to each parameter.

If driven_only is true, then count will be the number of unique driven torsions, rather than unique molecules.

filter(*filters)

Filter this collection by applying a set of filters sequentially, returning a new collection containing only the retained entries.

Notes

Information about any applied filters will be stored in the provenance dictionary in the ‘applied-filters’ field. Any existing information in this field will be overwritten here.

Parameters:: filters (ResultFilter) – The filters to apply, in the order to apply them in.
Returns:: The collection containing only the retained entries.

Example:

>>> filtered_collection = self.filter(
>>>     RecordStatusFilter(status=RecordStatusEnum.complete),
>>>     ConnectivityFilter(tolerance=1.2),
>>>     UnperceivableStereoFilter(),
>>>     ElementFilter(allowed_elements=elements),
>>>     ConformerRMSDFilter(max_conformers=max_opt_conformers),
>>> )

property n_molecules: int: Returns the number of unique molecules referenced by this collection.

property n_results: int: Returns the number of results in this collection.