qblox
=====

.. py:module:: quantify_scheduler.qblox 

.. autoapi-nested-parse::

   Module containing commonly used qblox specific classes.



Subpackages
-----------
.. toctree::
   :titlesonly:
   :maxdepth: 3

   operations/index.rst


Package Contents
----------------

Classes
~~~~~~~

.. autoapisummary::

   quantify_scheduler.qblox.ClusterComponent



Functions
~~~~~~~~~

.. autoapisummary::

   quantify_scheduler.qblox.start_dummy_cluster_armed_sequencers



.. py:function:: start_dummy_cluster_armed_sequencers(cluster_component: quantify_scheduler.instrument_coordinator.components.qblox.ClusterComponent) -> None

   Starting all armed sequencers in a dummy cluster.

   Starting all armed sequencers via Cluster.start_sequencer() doesn't yet
   work with dummy acquisition data (verified it does work on hardware).
   Hence, we need still need to call start_sequencer() for all sequencers separately.
   TODO: qblox_instruments.ieee488_2.cluster_dummy_transport.ClusterDummyTransport
   See SE-441.


.. py:class:: ClusterComponent(instrument: qblox_instruments.Cluster)

   Bases: :py:obj:`quantify_scheduler.instrument_coordinator.components.base.InstrumentCoordinatorComponentBase`


   Class that represents an instrument coordinator component for a Qblox cluster.

   New instances of the ClusterComponent will automatically add installed
   modules using name `"<cluster_name>_module<slot>"`.

   :param instrument: Reference to the cluster driver object.


   .. py:attribute:: _cluster_modules
      :type:  dict[str, _ClusterModule]


   .. py:attribute:: _program


   .. py:attribute:: module_type_map


   .. py:property:: is_running
      :type: bool

      Returns true if any of the modules are currently running.


   .. py:method:: start() -> None

      Starts all the modules in the cluster.



   .. py:method:: stop() -> None

      Stops all the modules in the cluster.



   .. py:method:: _configure_cmm_settings(settings: dict[str, Any]) -> None

      Set all the settings of the Cluster Management Module.

      These setting have been
      provided by the backend.

      :param settings: A dictionary containing all the settings to set.



   .. py:method:: prepare(options: dict[str, dict]) -> None

      Prepares the cluster component for execution of a schedule.

      :param options: The compiled instructions to configure the cluster to.



   .. py:method:: retrieve_acquisition() -> xarray.Dataset | None

      Retrieves all the data from the instruments.

      :returns: The acquired data or ``None`` if no acquisitions have been performed.



   .. py:method:: wait_done(timeout_sec: int = 10) -> None

      Blocks until all the components are done executing their programs.

      :param timeout_sec: The time in seconds until the instrument is considered to have timed out.



   .. py:method:: get_hardware_log(compiled_schedule: quantify_scheduler.schedules.schedule.CompiledSchedule) -> dict | None

      Retrieve the hardware log of the Cluster Management Module and associated modules.

      This log includes the module serial numbers and
      firmware version.

      :param compiled_schedule: Compiled schedule to check if this cluster is referenced in (and if so,
                                which specific modules are referenced in).

      :returns: A dict containing the hardware log of the cluster, in case the
                component was referenced; else None.