nanotune.data.databases

nanotune.data.databases.get_dataIDs(db_name: str, category: str, db_folder: Optional[str] = None, quality: Optional[int] = None, get_run_ids: bool = True) → List[int]

Returns QCoDeS run IDs of datasets belonging to a specific category of measurements, assigned to the data during labelling.

The entire database db_name is searched and a column of the same name as the category looked up needs to exist in it. Example of categories are pinchoff, singledot or doubledot, all valid ones are defined in configuration.config.json under the “labels” key. Optionally, the quality can be specified. Quality and category together are the machine learning labels nanotune uses during classification.

Parameters

• db_name – name of database to search.

• category – measurement type/category we are looking for. E.g. pinchoff, singledot or doubledot.

• db_folder – folder containing database. If not specified, nt.config[“db_folder”] is used.

• quality – Optional if a specific quality is required. 1==good, 0==poor.

• get_run_ids – whether to return run IDs. Returns captured run IDs if False.

Returns

List of QCoDeS run IDs.

nanotune.data.databases.get_unlabelled_ids(db_name: str, db_folder: Optional[str] = None, get_run_id: bool = True) → List[int]

Gets run IDs all unlabelled datasets in a database. A dataset is selected if it doesn’t have a value in the good column.

Parameters

• db_name – name of database to search.

• db_folder – folder containing database. If not specified, nt.config[“db_folder”] is used.

• get_run_id – whether to return run IDs. Returns captured run IDs if False.

Returns

List of QCoDeS run IDs which do not have a machine learning label.

nanotune.data.databases.list_experiments(db_folder: Optional[str] = None) → Tuple[str, Dict[str, List[int]]]

Lists all databases and their experiments within a folder.

Parameters

db_folder – the folder to enumerate.

Returns

• str – name of folder which was searched.

• dict – mapping database name to a list of experiment IDs.

nanotune.data.databases.new_database(db_name: str, db_folder: Optional[str] = None) → str

Create new database and initialise it with nanotune labels. A separate column for each label is created.

Parameters

• db_name – name of new database.

• db_folder – folder containing database. If not specified, nt.config[“db_folder”] is used.

Returns

str – absolute path of new database.

nanotune.data.databases.set_database(db_name: str, db_folder: Optional[str] = None) → None

Sets a new database to be the default one to load from and save to. If the database does not exist, a new one is created. The change is propagated to QCoDeS’ configuration.

Parameters

• db_name – name of database to set.

• db_folder – folder containing database. If not specified, nt.config[“db_folder”] is used.

nanotune.data.databases.get_database() → Tuple[str, str]

Gets name and folder of the current default database used by QCoDeS.

Returns

• str – database name

• str – folder containing database

nanotune.data.databases.get_last_dataid(db_name: str, db_folder: Optional[str] = None) → int

Returns QCoDeS run ID of last dataset in a database, i.e the highest run ID there is.

Parameters

• db_name – name of database.

• db_folder – folder containing database. If not specified, nt.config[“db_folder”] is used.

Returns

int – sum of last_counter of all experiments within a database.

nanotune.data.databases.switch_database(temp_db_name: str, temp_db_folder: str)

Context manager temporarily setting a different database. Sets back to previous database name and folder.

Parameters

• temp_db_name – name of database to set.

• temp_db_folder – folder containing database. If not specified, nt.config[“db_folder”] is used.