spinqick.experiments.psb_setup
==============================

.. py:module:: spinqick.experiments.psb_setup

.. autoapi-nested-parse::

   Defines the PsbSetup class which is used for setting up Pauli Spin Blockade spin-to-charge
   conversion.



Attributes
----------

.. autoapisummary::

   spinqick.experiments.psb_setup.logger


Classes
-------

.. autoapisummary::

   spinqick.experiments.psb_setup.PsbSetup


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

.. py:data:: logger

.. py:class:: PsbSetup(soccfg, soc, voltage_source, **kwargs)

   Bases: :py:obj:`spinqick.core.dot_experiment.DotExperiment`


   This class holds functions that wrap the QICK classes for setting up PSB readout. Initialize
   with information about your rfsoc and your experimental setup.

   :param soccfg: qick config object (QickConfig)
   :param soc: QickSoc object
   :param voltage_source: Initialized DC voltage source object. This is used here for saving the DC
       voltage state each time data is saved.


   .. py:attribute:: soccfg


   .. py:attribute:: soc


   .. py:attribute:: reference
      :value: True



   .. py:attribute:: thresh
      :value: True



   .. py:attribute:: threshold
      :value: 0



   .. py:attribute:: vdc


   .. py:property:: psb_config

      Get psb config for selected qubit from experiment config.


   .. py:method:: meashist(num_measurements, reference = True, fit = True, n_gaussians = 3, use_gmm = False, log_scale = False)

      Perform some number of spam sequences and produce a measurement histogram to show
      relative prevalance of measured 0 and 1 states.  This simply plays the spam sequence as
      defined in the config.

      :param num_measurements: Total number of measurements
      :param reference: perform a reference measurement and report the difference between the two
          measurements
      :param fit: if True, fit results to a pair of gaussians
      :param use_gmm: use gaussian mixtures model to fit gaussians :log_scale: if true, plot y
          axis in log scale



   .. py:method:: idle_cell_scan(p_gates, p_range, point_avgs = 10, full_avgs = 10)

      Performs a 2D sweep of idle coordinate.

      :param p_gates: specify the two plunger gates being used
      :param p_range: specify the range of each axis sweep. ((px_start, px_stop, px_points),
          (py_start, py_stop, py_points))



   .. py:method:: flush_window_scan(p_gates, p_range, point_avgs = 10, full_avgs = 10)

      Performs a 2D sweep of flush coordinate.

      :param p_gates: specify the two plunger gates being used
      :param p_range: specify the range of each axis sweep. ((px_start, px_stop, px_points),
          (py_start, py_stop, py_points))



   .. py:method:: meas_window_scan(p_gates, p_range, step_time, point_avgs = 10, full_avgs = 10)

      Performs a 2D sweep of the measurement coordinate.  Here we are using a series of fast
      steps to approximate a ramp.

      :param p_gates: specify the two plunger gates being used
      :param p_range: specify the range of each axis sweep. ((px_start, px_stop, px_points),
          (py_start, py_stop, py_points))
      :param step_time: time per step in each ramp, in microseconds. This uses a series of steps
          to approximate a ramp because it is challenging to code a sweep of the start and
          endpoints of a ramp in qick api.