create_reference_files.py

from __future__ import print_function
import pygsti
from pygsti.construction import std1Q_XYI
import pickle, os

try:
    basestring #Only defined in Python 2
    versionsuffix = ".v2" #Python 2
except NameError:
    versionsuffix = ".v3" #Python 3

pv = pygsti.__version__
if len(pv.split('.')) > 3:
    pv = '.'.join(pv.split('.')[0:3])
print("PyGSTi version ",pv)

target_model = std1Q_XYI.target_model()

# 2) get the building blocks needed to specify which operation sequences are needed
prep_fiducials, meas_fiducials = std1Q_XYI.prepStrs, std1Q_XYI.effectStrs
germs = std1Q_XYI.germs
maxLengths = [1,2,4] # roughly gives the length of the sequences used by GST

# 3) generate "fake" data from a depolarized version of target_model
mdl_datagen = target_model.depolarize(op_noise=0.01, spam_noise=0.001)
listOfExperiments = pygsti.construction.make_lsgst_experiment_list(
    target_model, prep_fiducials, meas_fiducials, germs, maxLengths)
ds = pygsti.construction.generate_fake_data(mdl_datagen, listOfExperiments, nSamples=1000,
                                            sampleError="binomial", seed=1234)

results = pygsti.do_stdpractice_gst(ds, target_model, prep_fiducials, meas_fiducials, 
                                    germs, maxLengths, verbosity=3)

def outname(typ, vs=True):
    nm = "pygsti" + pv + "." + typ + (versionsuffix if vs else "")
    if os.path.exists(nm):
        raise ValueError("File %s already exists! Will not overwrite it - you must remove it first." % nm)
    print("Writing ",nm)
    return nm

if versionsuffix == "v3": # only write text files for version 3 case
    pygsti.io.write_dataset(outname("dataset.txt",False), ds) # text dataset
ds.save(outname("dataset")) # binary dataset
with open(outname("dataset.pkl"),"wb") as f: # pickled dataset
    pickle.dump(ds, f)

if versionsuffix == "v3": # only write text files for version 3 case
    pygsti.io.write_model(mdl_datagen, outname("gateset.txt",False)) # text model
with open(outname("gateset.pkl"),"wb") as f: # pickled model
    pickle.dump(mdl_datagen, f)

with open(outname("results.pkl"),"wb") as f: # pickled results
    pickle.dump(results, f)