nanotune.tuningstages.chargediagram

class nanotune.tuningstages.chargediagram.RangeChangeSettingsDict(**kwargs)

Bases: dict

relative_range_change: float

min_change: float

max_change: float

class nanotune.tuningstages.chargediagram.ChargeDiagram(data_settings: DataSettings, setpoint_settings: SetpointSettings, readout: Readout, classifiers: Classifiers, target_regime: str = 'doubledot', range_change_settings: Optional[RangeChangeSettingsDict] = None)

Bases: TuningStage

Tuning stage measuring charge stability diagrams.

Parameters

• stage – String identifier indicating which stage it implements, e.g. gatecharacterization.

• data_settings – Dictionary with information about data, e.g. where it should be saved and how it should be normalized. Required fields are ‘db_name’, ‘db_folder’ and ‘normalization_constants’, ‘segment_size’.

• setpoint_settings – Dictionary with information about how to compute setpoints. Required keys are ‘parameters_to_sweep’, ‘safety_voltages’, ‘current_valid_ranges’ and ‘voltage_precision’.

• readout –

• current_valid_ranges – List of voltages ranges (tuples of floats) to measure.

• safety_voltage_ranges – List of satefy voltages ranges, i.e. safety limits within the device stays alive.

• target_regime – String indicating the desired final charge state/dot regime.

• range_change_settings – Dictionary with keys ‘relative_range_change’, ‘min_change’, ‘max_change’. Used to determine new voltage ranges to sweep.

• classifiers – Pre-trained nt.Classifiers predicting charge states and their quality.

• fit_class – Returns the class used to perform data fitting, i.e. nanotune.fit.dotfit.Dotfit.

property fit_class

Returns nanotune’s Dotfit

conclude_iteration(tuning_result: TuningResult, current_valid_ranges: Sequence[Sequence[float]], safety_voltage_ranges: Sequence[Sequence[float]], current_iteration: int, max_n_iterations: int) → Tuple[bool, Sequence[Sequence[float]], List[str]]

Method checking if one iteration of a run_stage measurement cycle has been successful.

An iteration of such a measurement cycle takes data, performs a machine learning task, verifies and saves the machine learning result. If a repetition of this cycle is supported, then conclude_iteration determines whether another iteration should take place and which voltage ranges need to be measured. Each child class needs to implement the body of this method, tailoring it to the respective tuning stage.

Parameters

• tuning_result – Result of the last run_stage measurement cycle.

• current_valid_ranges – Voltage ranges last swept.

• safety_voltage_ranges – Safety voltage ranges, i.e. largest possible range that could be swept.

• current_iteration – Number of current iteration.

• max_n_iterations – Maximum number of iterations to perform before abandoning.

Returns

• bool – Whether this is the last iteration and the stage is done/to be stopped.

• list – New voltage ranges to sweep if the stage is not done.

• list – List of strings indicating failure modes.

verify_machine_learning_result(ml_result: Dict[str, Any]) → bool

Verifies if the desired regime and quality have been found.

Parameters

ml_result – Dictionary returned by self.machine_learning_task.

Returns

bool – Whether the desired outcome has been found.

machine_learning_task(run_id: int) → Dict[str, Any]

Divides original measurement into segments, which are classified seperately. The overall outcome, i.e. regime and quality, are determined using conclude_dot_classification defined in .chargediagram_tasks. If the desired regime is not found, the overall regime is the most frequent one in the dot segments.

Parameters

run_id – QCoDeS data run ID.

Returns

dict – The classification outcome, both segment wise under the key ‘dot_segments’ as well as the overall outcome under ‘regime’ and ‘quality’.

get_range_update_directives(run_id: int, current_valid_ranges: Sequence[Sequence[float]], safety_voltage_ranges: Sequence[Sequence[float]]) → Tuple[List[str], List[str]]

Determines directives indicating if the current voltage ranges need to be extended or shifted. It first gets these directives from the data fit using get_fit_range_update_directives defined in .base_tasks.py and then checks if they can be put into action using get_range_directives_chargediagram defined in chargediagram_tasks.py. The check looks at whether safety ranges have been reached already, or whether a voltage range extension is possible.

Parameters

• run_id – QCoDeS data run ID.

• current_valid_ranges – Last voltage range swept.

• safety_voltage_ranges – Safety range of gate swept.

Returns

• list – List with range update directives.

• list – List with issues encountered.