Module containing quantify_core.measurement.types.Gettables for use with quantify-scheduler.


The gettable module is expected to change significantly as the acquisition protocols (#36 and #80) get fully supported by the scheduler. Currently different Gettables are required for different acquisition modes. The intent is to have one generic ScheduleGettable. Expect breaking changes.

Module Contents#



Generic gettable for a quantify schedule using vector (I,Q) acquisition.


_evaluate_parameter_dict(→ dict[str, Any])

Loop over the keys and values in a dict and replaces parameters with their current



exception AcquisitionProtocolError[source]#

Bases: TypeError

Inappropriate argument type.

exception AcquisitionProtocolNotSupportedError[source]#

Bases: NotImplementedError

Method or function hasn’t been implemented yet.

class ScheduleGettable(quantum_device: quantify_scheduler.device_under_test.quantum_device.QuantumDevice, schedule_function: Callable[Ellipsis, quantify_scheduler.Schedule], schedule_kwargs: dict[str, Any], num_channels: int = 1, data_labels: list[str] | None = None, real_imag: bool = True, batched: bool = False, max_batch_size: int = 1024, always_initialize: bool = True)[source]#

Generic gettable for a quantify schedule using vector (I,Q) acquisition.

The gettable evaluates the parameters passed as schedule_kwargs, then generates the quantify_scheduler.schedules.schedule.Schedule using the schedule_function, this is then compiled and finally executed by the InstrumentCoordinator.

ScheduleGettable can be set to return either static (demodulated) I and Q values or magnitude and phase.

  • quantum_device – The qcodes instrument representing the quantum device under test (DUT) containing quantum device properties and setup configuration information.

  • schedule_function – A function which returns a quantify_scheduler.schedules.schedule.Schedule. The function is required to have the repetitions keyword argument.

  • schedule_kwargs – The schedule function keyword arguments, when a value in this dictionary is a Parameter, this parameter will be evaluated every time .get() is called before being passed to the schedule_function.

  • num_channels – The number of channels to expect in the acquisition data.

  • data_labels – Allows to specify custom labels. Needs to be precisely 2*num_channels if specified. The order is [Voltage I 0, Voltage Q 0, Voltage I 1, Voltage Q 1, …], in case real_imag==True, otherwise [Magnitude 0, Phase 0, Magnitude 1, Phase 1, …].

  • real_imag – If true, the gettable returns I, Q values. Otherwise, magnitude and phase (degrees) are returned.

  • batched – Used to indicate if the experiment is performed in batches or in an iterative fashion.

  • max_batch_size – Determines the maximum number of points to acquire when acquiring in batched mode. Can be used to split up a program in parts if required due to hardware constraints.

  • always_initialize – If True, then reinitialize the schedule on each invocation of get. If False, then only initialize the first invocation of get.

property compiled_schedule: quantify_scheduler.CompiledSchedule | None[source]#

Return the schedule used in this class

_compile(sched: quantify_scheduler.Schedule) None[source]#

Compile schedule, separated to allow for profiling compilation duration.

initialize() None[source]#

This generates the schedule and uploads the compiled instructions to the hardware using the instrument coordinator.

get() tuple[numpy.ndarray, Ellipsis][source]#

Start the experimental sequence and retrieve acquisition data.

The data format returned is dependent on the type of acquisitions used in the schedule. These data formats can be found in the user guide.


A tuple of acquisition data per acquisition channel as specified above.

_reshape_data(acq_metadata: quantify_scheduler.helpers.schedule.AcquisitionMetadata, vals: numpy.typing.NDArray) list[numpy.typing.NDArray][source]#
_process_acquired_data_trigger_count(acquired_data: xarray.Dataset, acq_metadata: quantify_scheduler.helpers.schedule.AcquisitionMetadata) dict[int, numpy.ndarray][source]#

Reformat acquired data in a dictionary. Used by process_acquired_data.

  • acquired_data – Acquired data as returned by instrument coordinator

  • acq_metadata – Acquisition metadata from schedule


Dictionary with reformatted data. Keys correspond to the acquisition channel. Values are 1d numpy arrays with trigger counts.


NotImplementedError – If acquisition protocol other than BinMode.APPEND is used.

process_acquired_data(acquired_data: xarray.Dataset, acq_metadata: quantify_scheduler.helpers.schedule.AcquisitionMetadata, repetitions: int | None = None) tuple[numpy.typing.NDArray[numpy.float_], Ellipsis][source]#

Reshapes the data as returned from the instrument coordinator into the form accepted by the measurement control.

  • acquired_data – Data that is returned by instrument coordinator.

  • acq_metadata – Acquisition metadata from schedule. It is supposed to be generated by extract_acquisition_metadata_from_schedule().

  • repetitions – (deprecated) Should not be specified. Will be removed in quantify-scheduler-0.16.


A tuple of data, casted to a historical conventions on data format.

initialize_and_get_with_report() str[source]#

Create a report that saves all information from this experiment in a zipfile.

Run initialize() and get() and capture all information from the experiment in a zipfile in the quantify datadir. The basic information in the report includes the schedule, device config and hardware config. The method attempts to compile the schedule, and if it succeeds, it runs the experiment and adds the compiled schedule, a snapshot of the instruments, and logs from the actual hardware (only Qblox instruments supported currently) to the zipfile. A full error trace is also included if any of these steps fail.


A path to the generated report. Directory name includes a flag indicating at which state the experiment and report retrieval stopped.

Flags (defined in :func: ~._generate_diagnostics_report):

  • failed_initialization: The experiment failed during initialize().

  • failed_exp: The experiment initialized failed during get().

  • failed_connection_to_hw: The experiment initialized but both get() and retrieve_hardware_logs() failed. Connection to hardware was likely interrupted during runtime.

  • failed_hw_log_retrieval: The experiment succeeded but retrieve_hardware_logs() failed.

  • completed_exp: The experiment succeeded.

generate_diagnostics_report(execute_get: bool = False, update: bool = False) str[source]#

Create a report that saves all information contained in this ScheduleGettable and save it in the quantify datadir with its own tuid. The information in the report includes the generated schedule, device config, hardware config and snapshot of the instruments.

  • execute_get – When True, executes self.get() before generating the report.

  • update – When True, updates all parameters before saving the snapshot.


The tuid of the generated report.

_evaluate_parameter_dict(parameters: dict[str, Any]) dict[str, Any][source]#

Loop over the keys and values in a dict and replaces parameters with their current value.


parameters – A dictionary containing a mix of Parameters and normal values.


The parameters dictionary, but with the parameters replaced by their current value.


TypeError – If a parameter returns None