node

tergite_autocalibration.lib.base.node

Classes:

Name	Description
`BaseNode`
`CouplerNode`

BaseNode

BaseNode(**node_dictionary)

Bases: ABC

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

CouplerNode

CouplerNode(couplers: list[str], **node_keywords)

Bases: BaseNode

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
`set_parking_current_from_redis`	At the beginning of the calibration, the parking current is set by

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

Overwrite the base method, to set the updated SPI currents before the measurement.

set_parking_current_from_redis

set_parking_current_from_redis()

At the beginning of the calibration, the parking current is set by the calibration supervisor from the device_config value. This value can be updated by the cz_parametrization measurement which updates the parking current value on redis.

This method fetches the redis value and sets the update DC current to the appropriate SPI dacs.