:py:mod:`pygsti.data.rpedata`
=============================

.. py:module:: pygsti.data.rpedata

.. autoapi-nested-parse::

   Functions for creating RPE data sets






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


Functions
~~~~~~~~~

.. autoapisummary::

   pygsti.data.rpedata.make_rpe_data_set



.. py:function:: make_rpe_data_set(model_or_dataset, string_list_d, num_samples, sample_error='binomial', seed=None)

   Generate a fake RPE DataSet using the probabilities obtained from a model.

   Is a thin wrapper for pygsti.data.simulate_data, changing
   default behavior of sample_error, and taking a dictionary of circuits
   as input.

   Parameters
   ----------
   model_or_dataset : Model or DataSet object
       If a Model, the model whose probabilities generate the data.
       If a DataSet, the data set whose frequencies generate the data.

   string_list_d : Dictionary of list of (tuples or Circuits)
       Each tuple or Circuit contains operation labels and
       specifies a gate sequence whose counts are included
       in the returned DataSet.  The dictionary must have the key
       'totalStrList'; easiest if this dictionary is generated by
       make_rpe_string_list_d.

   num_samples : int or list of ints or None
       The simulated number of samples for each circuit.  This only
       has effect when  sample_error == "binomial" or "multinomial".  If
       an integer, all circuits have this number of total samples. If
       a list, integer elements specify the number of samples for the
       corresponding circuit.  If None, then model_or_dataset must be
       a DataSet, and total counts are taken from it (on a per-circuit
       basis).

   sample_error : string, optional
       What type of sample error is included in the counts.  Can be:

       - "none"  - no sample error:
         counts are floating point numbers such that the exact
         probability can be found by the ratio of count / total.
       - "round" - same as "none", except counts are rounded to the nearest
         integer.
       - "binomial" - the number of counts is taken from a binomial
         distribution. Distribution has parameters p = probability of the
         circuit and n = number of samples.  This can only be used when
         there are exactly two SPAM labels in model_or_dataset.
       - "multinomial" - counts are taken from a multinomial distribution.
         Distribution has parameters p_k = probability of the circuit
         using the k-th SPAM label and n = number of samples.  This should not
         be used for RPE.

   seed : int, optional
       If not None, a seed for numpy's random number generator, which
       is used to sample from the binomial or multinomial distribution.

   Returns
   -------
   DataSet
       A static data set filled with counts for the specified circuits.