/
reportBaseCase.py
159 lines (128 loc) · 7.61 KB
/
reportBaseCase.py
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import unittest
import warnings
import pickle
import collections
import pygsti
import os
from pygsti.modelpacks.legacy import std1Q_XYI as std
from ..testutils import BaseTestCase, compare_files, temp_files, regenerate_references
import numpy as np
class ReportBaseCase(BaseTestCase):
@classmethod
def setUpClass(cls):
"""
Handle all once-per-class (slow) computation and loading,
to avoid calling it for each test (like setUp). Store
results in class variable for use within setUp.
"""
super(ReportBaseCase, cls).setUpClass()
orig_cwd = os.getcwd()
os.chdir(os.path.abspath(os.path.dirname(__file__)))
os.chdir('..') # The test_packages directory
targetModel = std.target_model()
datagen_gateset = targetModel.depolarize(op_noise=0.05, spam_noise=0.1)
datagen_gateset2 = targetModel.depolarize(op_noise=0.1, spam_noise=0.05).rotate((0.15,-0.03,0.03))
#cls.specs = pygsti.construction.build_spam_specs(std.fiducials, effect_labels=['E0'])
# #only use the first EVec
opLabels = std.gates
cls.lgstStrings = pygsti.construction.list_lgst_circuits(std.fiducials, std.fiducials, opLabels)
cls.maxLengthList = [1,2,4,8]
cls.lsgstStrings = pygsti.construction.make_lsgst_lists(
opLabels, std.fiducials, std.fiducials, std.germs, cls.maxLengthList)
cls.lsgstStructs = pygsti.construction.make_lsgst_structs(
opLabels, std.fiducials, std.fiducials, std.germs, cls.maxLengthList)
# RUN BELOW LINES TO GENERATE ANALYSIS DATASET (SAVE)
if regenerate_references():
ds = pygsti.construction.generate_fake_data(datagen_gateset, cls.lsgstStrings[-1], nSamples=1000,
sampleError='binomial', seed=100)
ds.save(compare_files + "/reportgen.dataset")
ds2 = pygsti.construction.generate_fake_data(datagen_gateset2, cls.lsgstStrings[-1], nSamples=1000,
sampleError='binomial', seed=100)
ds2.save(compare_files + "/reportgen2.dataset")
cls.ds = pygsti.objects.DataSet(fileToLoadFrom=compare_files + "/reportgen.dataset")
cls.ds2 = pygsti.objects.DataSet(fileToLoadFrom=compare_files + "/reportgen2.dataset")
mdl_lgst = pygsti.do_lgst(cls.ds, std.fiducials, std.fiducials, targetModel, svdTruncateTo=4, verbosity=0)
mdl_lgst_go = pygsti.gaugeopt_to_target(mdl_lgst, targetModel, {'gates': 1.0, 'spam': 0.0})
cls.mdl_clgst = pygsti.contract(mdl_lgst_go, "CPTP")
cls.mdl_clgst_tp = pygsti.contract(cls.mdl_clgst, "vSPAM")
cls.mdl_clgst_tp.set_all_parameterizations("TP")
#Compute results for MC2GST
lsgst_gatesets_prego = pygsti.do_iterative_mc2gst(
cls.ds, cls.mdl_clgst, cls.lsgstStrings, verbosity=0,
minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6),
returnAll=True)
cls.results = pygsti.objects.Results()
cls.results.init_dataset(cls.ds)
cls.results.init_circuits(cls.lsgstStructs)
cls.results.add_estimate(targetModel, cls.mdl_clgst,
lsgst_gatesets_prego,
{'objective': "chi2",
'minProbClipForWeighting': 1e-4,
'probClipInterval': (-1e6,1e6), 'radius': 1e-4,
'weights': None, 'defaultDirectory': temp_files + "",
'defaultBasename': "MyDefaultReportName"})
gaugeOptParams = collections.OrderedDict([
('model', lsgst_gatesets_prego[-1]), #so can gauge-propagate CIs
('targetModel', targetModel), #so can gauge-propagate CIs
('cptp_penalty_factor', 0),
('gatesMetric',"frobenius"),
('spamMetric',"frobenius"),
('itemWeights', {'gates': 1.0, 'spam': 0.001}),
('returnAll', True) ])
_, gaugeEl, go_final_gateset = pygsti.gaugeopt_to_target(**gaugeOptParams)
gaugeOptParams['_gaugeGroupEl'] = gaugeEl #so can gauge-propagate CIs
cls.results.estimates['default'].add_gaugeoptimized(gaugeOptParams, go_final_gateset)
cls.results.estimates['default'].add_gaugeoptimized(gaugeOptParams, go_final_gateset, "go_dup")
#Compute results for MLGST with TP constraint
# Use do_long_sequence_gst with a non-mark dataset to trigger data scaling
tp_target = targetModel.copy(); tp_target.set_all_parameterizations("TP")
cls.ds3 = cls.ds.copy_nonstatic()
cls.ds3.add_counts_from_dataset(cls.ds2)
cls.ds3.done_adding_data()
cls.results_logL = pygsti.do_long_sequence_gst(cls.ds3, tp_target, std.fiducials, std.fiducials,
std.germs, cls.maxLengthList, verbosity=0,
advancedOptions={'tolerance': 1e-6, 'starting point': 'LGST',
'onBadFit': ["robust","Robust","robust+","Robust+"],
'badFitThreshold': -1.0,
'germLengthLimits': {('Gx','Gi','Gi'): 2} })
#OLD
#lsgst_gatesets_TP = pygsti.do_iterative_mlgst(cls.ds, cls.mdl_clgst_tp, cls.lsgstStrings, verbosity=0,
# minProbClip=1e-4, probClipInterval=(-1e6,1e6),
# returnAll=True) #TP initial model => TP output models
#cls.results_logL = pygsti.objects.Results()
#cls.results_logL.init_dataset(cls.ds)
#cls.results_logL.init_circuits(cls.lsgstStructs)
#cls.results_logL.add_estimate(targetModel, cls.mdl_clgst_tp,
# lsgst_gatesets_TP,
# {'objective': "logl",
# 'minProbClip': 1e-4,
# 'probClipInterval': (-1e6,1e6), 'radius': 1e-4,
# 'weights': None, 'defaultDirectory': temp_files + "",
# 'defaultBasename': "MyDefaultReportName"})
#
#tp_target = targetModel.copy(); tp_target.set_all_parameterizations("TP")
#gaugeOptParams = gaugeOptParams.copy() #just to be safe
#gaugeOptParams['model'] = lsgst_gatesets_TP[-1] #so can gauge-propagate CIs
#gaugeOptParams['targetModel'] = tp_target #so can gauge-propagate CIs
#_, gaugeEl, go_final_gateset = pygsti.gaugeopt_to_target(**gaugeOptParams)
#gaugeOptParams['_gaugeGroupEl'] = gaugeEl #so can gauge-propagate CIs
#cls.results_logL.estimates['default'].add_gaugeoptimized(gaugeOptParams, go_final_gateset)
#
##self.results_logL.options.precision = 3
##self.results_logL.options.polar_precision = 2
os.chdir(orig_cwd)
def setUp(self):
super(ReportBaseCase, self).setUp()
cls = self.__class__
self.targetModel = std.target_model()
self.fiducials = std.fiducials[:]
self.germs = std.germs[:]
self.opLabels = std.gates
#self.specs = cls.specs
self.maxLengthList = cls.maxLengthList[:]
self.lgstStrings = cls.lgstStrings
self.ds = cls.ds
self.mdl_clgst = cls.mdl_clgst.copy()
self.mdl_clgst_tp = cls.mdl_clgst_tp.copy()
self.results = cls.results.copy()
self.results_logL = cls.results_logL.copy()