corrections
===========

.. py:module:: quantify_scheduler.backends.corrections 

.. autoapi-nested-parse::

   Pulse and acquisition corrections for hardware compilation.



Module Contents
---------------


Functions
~~~~~~~~~

.. autoapisummary::

   quantify_scheduler.backends.corrections.determine_relative_latency_corrections
   quantify_scheduler.backends.corrections.distortion_correct_pulse
   quantify_scheduler.backends.corrections._is_distortion_correctable
   quantify_scheduler.backends.corrections.apply_software_distortion_corrections



Attributes
~~~~~~~~~~

.. autoapisummary::

   quantify_scheduler.backends.corrections.logger


.. py:data:: logger

.. py:function:: determine_relative_latency_corrections(hardware_cfg: quantify_scheduler.backends.types.common.HardwareCompilationConfig | dict[str, Any], schedule: quantify_scheduler.schedules.schedule.Schedule | None = None) -> dict[str, float]

   Generates the latency configuration dict for all port-clock combinations that are present in
   the schedule (or in the hardware config, if an old-style zhinst config is passed). This is done by first setting unspecified latency corrections to zero, and then
   subtracting the minimum latency from all latency corrections.


.. py:function:: distortion_correct_pulse(pulse_data: dict[str, Any], distortion_correction: quantify_scheduler.backends.types.common.SoftwareDistortionCorrection) -> quantify_scheduler.operations.pulse_library.NumericalPulse

   Sample pulse and apply filter function to the sample to distortion correct it.

   :param pulse_data: Definition of the pulse.
   :param distortion_correction: The distortion_correction configuration for this pulse.

   :returns: The sampled, distortion corrected pulse wrapped in a ``NumericalPulse``.


.. py:function:: _is_distortion_correctable(operation: quantify_scheduler.operations.operation.Operation) -> bool

   Checks whether distortion corrections can be applied to the given operation.


.. py:function:: apply_software_distortion_corrections(operation: quantify_scheduler.operations.operation.Operation | quantify_scheduler.schedules.schedule.Schedule, distortion_corrections: dict) -> quantify_scheduler.operations.operation.Operation | quantify_scheduler.schedules.schedule.Schedule | None

   Apply distortion corrections to operations in the schedule.

   Defined via the hardware configuration file, example:

   .. code-block::

       "distortion_corrections": {
           "q0:fl-cl0.baseband": {
               "filter_func": "scipy.signal.lfilter",
               "input_var_name": "x",
               "kwargs": {
                   "b": [0.0, 0.5, 1.0],
                   "a": [1]
               },
               "clipping_values": [-2.5, 2.5]
           }
       }

   Clipping values are the boundaries to which the corrected pulses will be clipped,
   upon exceeding, these are optional to supply.

   For pulses in need of correcting (indicated by their port-clock combination) we are
   **only** replacing the dict in ``"pulse_info"`` associated to that specific
   pulse. This means that we can have a combination of corrected (i.e., pre-sampled)
   and uncorrected pulses in the same operation.

   Note that we are **not** updating the ``"operation_id"`` key, used to reference
   the operation from schedulables.

   :param operation: The operation that contains operations that are to be distortion corrected.
                     Note, this function updates the operation.
   :param distortion_corrections: The distortion_corrections configuration of the setup.

   :returns: The new operation with distortion corrected operations, if it needs to be replaced.
             If it doesn't need to be replaced in the schedule or control flow, it returns ``None``.

   :Warns: **RuntimeWarning** -- If distortion correction can not be applied to the type of Operation in the
           schedule.

   :raises KeyError: when elements are missing in distortion correction config for a port-clock
       combination.
   :raises KeyError: when clipping values are supplied but not two values exactly, min and max.