pygsti.data.multidataset.MultiDataSet

pygsti.data.multidataset.MultiDataSet#

class MultiDataSet(oli_dict=None, time_dict=None, rep_dict=None, circuit_indices=None, outcome_labels=None, outcome_label_indices=None, file_to_load_from=None, collision_actions=None, comment=None, comments=None, aux_info=None)#

Bases: object

A collection of DataSets that hold data for the same circuits.

The MultiDataSet class allows for the combined access and storage of several static DataSets that contain the same circuits (in the same order) AND the same time-dependence structure (if applicable).

It is designed to behave similarly to a dictionary of DataSets, so that a DataSet is obtained by:

dataset = multiDataset[dataset_name]

where dataset_name may be a string OR a tuple.

Parameters:

  • oli_dict (ordered dictionary, optional) – Keys specify dataset names. Values are 1D numpy arrays which specify outcome label indices. Each value is indexed by the values of circuit_indices.

  • time_dict (ordered dictionary, optional) – Same format as oli_dict except stores arrays of floating-point time stamp data.

  • rep_dict (ordered dictionary, optional) – Same format as oli_dict except stores arrays of integer repetition counts (can be None if there are no repetitions)

  • circuit_indices (ordered dictionary, optional) – An OrderedDict with keys equal to circuits (tuples of operation labels) and values equal to integer indices associating a row/element of counts with the circuit.

  • outcome_labels (list of strings) – Specifies the set of spam labels for the DataSet. Indices for the spam labels are assumed to ascend from 0, starting with the first element of this list. These indices will associate each elememtn of timeseries with a spam label. Only specify this argument OR outcome_label_indices, not both.

  • outcome_label_indices (ordered dictionary) – An OrderedDict with keys equal to spam labels (strings) and value equal to integer indices associating a spam label with given index. Only specify this argument OR outcome_labels, not both.

  • file_to_load_from (string or file object, optional) – Specify this argument and no others to create a MultiDataSet by loading from a file (just like using the load(…) function).

  • collision_actions (dictionary, optional) – Specifies how duplicate circuits should be handled for the data sets. Keys must match those of oli_dict and values are “aggregate” or “keepseparate”. See documentation for DataSet. If None, then “aggregate” is used for all sets by default.

  • comment (string, optional) – A user-specified comment string that gets carried around with the data. A common use for this field is to attach to the data details regarding its collection.

  • comments (dict, optional) – A user-specified dictionary of comments, one per dataset. Keys are dataset names (same as oli_dict keys).

  • aux_info (dict, optional) – A user-specified dictionary of per-circuit auxiliary information. Keys should be the circuits in this MultiDataSet and value should be Python dictionaries.

Initialize a MultiDataSet.

Parameters:

  • oli_dict (ordered dictionary, optional) – Keys specify dataset names. Values are 1D numpy arrays which specify outcome label indices. Each value is indexed by the values of circuit_indices.

  • time_dict (ordered dictionary, optional) – Same format as oli_dict except stores arrays of floating-point time stamp data.

  • rep_dict (ordered dictionary, optional) – Same format as oli_dict except stores arrays of integer repetition counts (can be None if there are no repetitions)

  • circuit_indices (ordered dictionary, optional) – An OrderedDict with keys equal to circuits (tuples of operation labels) and values equal to integer indices associating a row/element of counts with the circuit.

  • outcome_labels (list of strings) – Specifies the set of spam labels for the DataSet. Indices for the spam labels are assumed to ascend from 0, starting with the first element of this list. These indices will associate each elememtn of timeseries with a spam label. Only specify this argument OR outcome_label_indices, not both.

  • outcome_label_indices (ordered dictionary) – An OrderedDict with keys equal to spam labels (strings) and value equal to integer indices associating a spam label with given index. Only specify this argument OR outcome_labels, not both.

  • file_to_load_from (string or file object, optional) – Specify this argument and no others to create a MultiDataSet by loading from a file (just like using the load(…) function).

  • collision_actions (dictionary, optional) – Specifies how duplicate circuits should be handled for the data sets. Keys must match those of oli_dict and values are “aggregate” or “keepseparate”. See documentation for DataSet. If None, then “aggregate” is used for all sets by default.

  • comment (string, optional) – A user-specified comment string that gets carried around with the data. A common use for this field is to attach to the data details regarding its collection.

  • comments (dict, optional) – A user-specified dictionary of comments, one per dataset. Keys are dataset names (same as oli_dict keys).

  • aux_info (dict, optional) – A user-specified dictionary of per-circuit auxiliary information. Keys should be the circuits in this MultiDataSet and value should be Python dictionaries.

Returns:

a new multi data set object.

Return type:

MultiDataSet

Methods

__init__([oli_dict, time_dict, rep_dict, ...])

Initialize a MultiDataSet.

add_auxiliary_info(circuit, aux)

Add auxiliary meta information to circuit.

add_dataset(dataset_name, dataset[, ...])

Add a DataSet to this MultiDataSet.

copy()

Make a copy of this MultiDataSet

datasets_aggregate(*dataset_names)

Generate a new DataSet by combining the outcome counts of multiple member Datasets.

items()

Iterator over (dataset name, DataSet) pairs.

keys()

A list of the keys (dataset names) of this MultiDataSet

load(**kwargs)

read_binary(file_or_filename)

Read a MultiDataSet from a file, clearing any data is contained previously.

save(**kwargs)

values()

Iterator over DataSets corresponding to each dataset name.

write_binary(file_or_filename)

Write this MultiDataSet to a binary-format file.

Attributes

outcome_labels

Get a list of all the outcome labels contained in this MultiDataSet.
add_auxiliary_info(circuit, aux)#

Add auxiliary meta information to circuit.

Parameters:

  • circuit (tuple or Circuit) – A tuple of operation labels specifying the circuit or a Circuit object

  • aux (dict, optional) – A dictionary of auxiliary meta information to be included with this set of data counts (associated with circuit).

Return type:

None

add_dataset(dataset_name, dataset, update_auxinfo=True)#

Add a DataSet to this MultiDataSet.

The dataset must be static and conform with the circuits and time-dependent structure passed upon construction or those inherited from the first dataset added.

Parameters:

  • dataset_name (string) – The name to give the added dataset (i.e. the key the new data set will be referenced by).

  • dataset (DataSet) – The data set to add.

  • update_auxinfo (bool, optional) – Whether the auxiliary information (if any exists) in dataset is added to the information already stored in this MultiDataSet.

Return type:

None

copy()#

Make a copy of this MultiDataSet

Return type:

MultiDataSet

datasets_aggregate(*dataset_names)#

Generate a new DataSet by combining the outcome counts of multiple member Datasets.

Data with the same time-stamp and outcome are merged into a single “bin” in the returned DataSet.

Parameters:

dataset_names (list of strs) – one or more dataset names.

Returns:

a single DataSet containing the summed counts of each of the data named by the parameters.

Return type:

DataSet

items()#

Iterator over (dataset name, DataSet) pairs.

keys()#

A list of the keys (dataset names) of this MultiDataSet

Return type:

list

read_binary(file_or_filename)#

Read a MultiDataSet from a file, clearing any data is contained previously.

The file should have been created with MultiDataSet.write_binary()

Parameters:

file_or_filename (file or string) – Either a filename or a file object. In the former case, if the filename ends in “.gz”, the file will be gzip uncompressed as it is read.

Return type:

None

values()#

Iterator over DataSets corresponding to each dataset name.

write_binary(file_or_filename)#

Write this MultiDataSet to a binary-format file.

Parameters:

file_or_filename (file or string) – Either a filename or a file object. In the former case, if the filename ends in “.gz”, the file will be gzip compressed.

Return type:

None

property outcome_labels#

Get a list of all the outcome labels contained in this MultiDataSet.

Returns:

A list where each element is an outcome label (which can be a string or a tuple of strings).

Return type:

list of strings or tuples