MultiDataSets

Sometimes it is useful to deal with several sets of data all of which hold counts for the same set of operation sequences. For example, colleting data to perform GST on Monday and then again on Tuesday, or making an adjustment to an experimental system and re-taking data, could create two separate data sets with the same sequences. PyGSTi has a separate data type, pygsti.objects.MultiDataSet, for this purpose. A MultiDataSet looks and acts like a simple dictionary of DataSet objects, but underneath implements some certain optimizations that reduce the amount of space and memory required to store the data. Primarily, it holds just a single list of the circuits - as opposed to an actual dictionary of DataSets in which each DataSet contains it’s own copy of the circuits. In addition to being more space efficient, a MultiDataSet is able to aggregate all of its data into a single “summed” DataSet via get_datasets_aggregate(...), which can be useful for combining several “passes” of experimental data.

Several remarks regarding a MultiDataSet are worth mentioning:

• you add DataSets to a MultiDataSet using the add_dataset method. However only static DataSet objects can be added. This is because the MultiDataSet must keep all of its DataSets locked to the same set of sequences, and a non-static DataSet allows the addition or removal of only its sequences. (If the DataSet you want to add isn’t in static-mode, call its done_adding_data method.)

• square-bracket indexing accesses the MultiDataSet as if it were a dictionary of DataSets.

• MultiDataSets can be loaded and saved from a single text-format file with columns for each contained DataSet - see pygsti.io.load_multidataset.

Here’s a brief example of using a MultiDataSet:

import pygsti

multiDS = pygsti.data.MultiDataSet()

#Create some datasets                                           
ds = pygsti.data.DataSet(outcome_labels=['0','1'])
ds.add_count_dict( (), {'0': 10, '1': 90} )
ds.add_count_dict( ('Gx',), {'0': 10, '1': 90} )
ds.add_count_dict( ('Gx','Gy'), {'0': 20, '1': 80} )
ds.add_count_dict( ('Gx','Gx','Gx','Gx'), {'0': 20, '1': 80} )
ds.done_adding_data()

ds2 = pygsti.data.DataSet(outcome_labels=['0','1'])            
ds2.add_count_dict( (), {'0': 15, '1': 85} )
ds2.add_count_dict( ('Gx',), {'0': 5, '1': 95} )
ds2.add_count_dict( ('Gx','Gy'), {'0': 30, '1': 70} )
ds2.add_count_dict( ('Gx','Gx','Gx','Gx'), {'0': 40, '1': 60} )
ds2.done_adding_data()

multiDS['myDS'] = ds
multiDS['myDS2'] = ds2

nStrs = len(multiDS)
dslabels = list(multiDS.keys())
print("MultiDataSet has %d operation sequences and DataSet labels %s" % (nStrs, dslabels))
    
for dslabel in multiDS:
    ds = multiDS[dslabel]
    print("Empty string data for %s = " % dslabel, ds[()])       

for ds in multiDS.values():
    print("Gx string data (no label) =", ds[('Gx',)])     

for dslabel,ds in multiDS.items():
    print("GxGy string data for %s =" % dslabel, ds[('Gx','Gy')])  

dsSum = multiDS.datasets_aggregate('myDS','myDS2')
print("\nSummed data:")
print(dsSum)

multi_dataset_txt = \
"""## Columns = DS0 0 count, DS0 1 count, DS1 0 frequency, DS1 count total                                
{} 0 100 0 100                                                                                                      
Gx 10 90 0.1 100                                                                                                    
GxGy 40 60 0.4 100                                                                                                  
Gx^4 20 80 0.2 100                                                                                                  
"""

with open("../../tutorial_files/TinyMultiDataset.txt","w") as output:
    output.write(multi_dataset_txt)
multiDS_fromFile = pygsti.io.read_multidataset("../../tutorial_files/TinyMultiDataset.txt", cache=False)

print("\nLoaded from file:\n")
print(multiDS_fromFile)

Those are the basics of using MultiDataSet. More information is available in the docstrings for the various MultiDataSet methods.