node

tergite_autocalibration.lib.nodes.characterization.t2.node

Classes:

Name	Description
`T2EchoNode`	Node for T2 Echo measurement and analysis.
`T2Node`	Node for T2 measurement and analysis.

T2EchoNode

T2EchoNode(all_qubits: list[str], **schedule_keywords)

Bases: QubitNode

Node for T2 Echo measurement and analysis. This node performs T2 Echo measurements on multiple qubits and analyzes the results to extract T2 Echo times. It uses the T2EchoMeasurement class for measurement and T2EchoNodeAnalysis for analysis.

Methods:

Name	Description
`configure_dataset`	The dataset retrieved from the instrument coordinator is
`measure_compiled_schedule`	Execute a measurement for a node and save the resulting dataset.
`measure_node`	Here we attach the measure_node method according to the

configure_dataset

configure_dataset(raw_ds: Dataset) -> Dataset

The dataset retrieved from the instrument coordinator is too bare-bones. Here the dims, coords and data_vars are configured

measure_compiled_schedule

measure_compiled_schedule(compiled_schedule: CompiledSchedule, measurement_mode=real, measurement: Tuple[int, int] = (1, 1)) -> Dataset

Execute a measurement for a node and save the resulting dataset.

Parameters:

Name	Type	Description	Default
`compiled_schedule`	`CompiledSchedule`	The compiled schedule to execute.	required
`measurement_mode`	`real`	The status of the measurement mode.	`real`
`measurement`	`tuple`	Tuple of (current_measurement, total_measurements).	`(1, 1)`

Returns:

Type	Description
`Dataset`	xarray.Dataset: The dataset containing the measurement results.

measure_node

measure_node(cluster_status) -> Dataset

Here we attach the measure_node method according to the measurement_type: ScheduleNode or ExternalParameterNode or something else

T2Node

T2Node(all_qubits: list[str], **schedule_keywords)