def runMC2GSTAnalysis(myspecs,
mygerms,
gsTarget,
seed,
maxLs=[1, 2, 4, 8],
nSamples=1000,
useFreqWeightedChiSq=False,
minProbClipForWeighting=1e-4,
fidPairList=None,
comm=None):
rhoStrs, EStrs = pygsti.construction.get_spam_strs(myspecs)
lgstStrings = pygsti.construction.list_lgst_gatestrings(
myspecs, gsTarget.gates.keys())
lsgstStrings = pygsti.construction.make_lsgst_lists(
gsTarget.gates.keys(), rhoStrs, EStrs, mygerms, maxLs, fidPairList)
print len(myspecs[0]), " rho specifiers"
print len(myspecs[1]), " effect specifiers"
print len(mygerms), " germs"
print len(lgstStrings), " total LGST gate strings"
print len(lsgstStrings[-1]), " LSGST strings before thinning"
lsgstStringsToUse = lsgstStrings
allRequiredStrs = pygsti.remove_duplicates(lgstStrings + lsgstStrings[-1])
gs_dataGen = gsTarget.depolarize(gate_noise=0.1)
dsFake = pygsti.construction.generate_fake_data(gs_dataGen,
allRequiredStrs,
nSamples,
sampleError="multinomial",
seed=seed)
#Run LGST to get starting gate set
gs_lgst = pygsti.do_lgst(dsFake,
myspecs,
gsTarget,
svdTruncateTo=gsTarget.dim,
verbosity=3)
gs_lgst_go = pygsti.optimize_gauge(gs_lgst,
"target",
targetGateset=gs_dataGen)
#Run full iterative LSGST
tStart = time.time()
all_gs_lsgst = pygsti.do_iterative_mc2gst(
dsFake,
gs_lgst_go,
lsgstStringsToUse,
minProbClipForWeighting=minProbClipForWeighting,
probClipInterval=(-1e5, 1e5),
verbosity=1,
memLimit=3 * (1024)**3,
returnAll=True,
useFreqWeightedChiSq=useFreqWeightedChiSq,
comm=comm)
tEnd = time.time()
print "Time = ", (tEnd - tStart) / 3600.0, "hours ( =", (tEnd -
tStart), " secs)"
return all_gs_lsgst, gs_dataGen
def mdl_lsgst(self):
return pygsti.do_iterative_mc2gst(self.dataset,
self.mdl_clgst,
self.lsgstStrings,
verbosity=0,
minProbClipForWeighting=1e-6,
probClipInterval=(-1e6, 1e6),
memLimit=self.CM + 1024**3)
def runAnalysis(obj,
ds,
myspecs,
gsTarget,
lsgstStringsToUse,
useFreqWeightedChiSq=False,
minProbClipForWeighting=1e-4,
fidPairList=None,
comm=None,
distributeMethod="gatestrings"):
#Run LGST to get starting gate set
assertGatesetsInSync(gsTarget, comm)
gs_lgst = pygsti.do_lgst(ds,
myspecs,
gsTarget,
svdTruncateTo=gsTarget.dim,
verbosity=3)
assertGatesetsInSync(gs_lgst, comm)
gs_lgst_go = pygsti.gaugeopt_to_target(gs_lgst, gsTarget)
assertGatesetsInSync(gs_lgst_go, comm)
#Run full iterative LSGST
tStart = time.time()
if obj == "chi2":
all_gs_lsgst = pygsti.do_iterative_mc2gst(
ds,
gs_lgst_go,
lsgstStringsToUse,
minProbClipForWeighting=minProbClipForWeighting,
probClipInterval=(-1e5, 1e5),
verbosity=1,
memLimit=3 * (1024)**3,
returnAll=True,
useFreqWeightedChiSq=useFreqWeightedChiSq,
comm=comm,
distributeMethod=distributeMethod)
elif obj == "logl":
all_gs_lsgst = pygsti.do_iterative_mlgst(
ds,
gs_lgst_go,
lsgstStringsToUse,
minProbClip=minProbClipForWeighting,
probClipInterval=(-1e5, 1e5),
verbosity=1,
memLimit=3 * (1024)**3,
returnAll=True,
useFreqWeightedChiSq=useFreqWeightedChiSq,
comm=comm,
distributeMethod=distributeMethod)
tEnd = time.time()
print("Time = ", (tEnd - tStart) / 3600.0, "hours")
return all_gs_lsgst
def runAnalysis(obj, myspecs, mygerms, gsTarget, seed,
maxLs = [1,2,4,8],
nSamples=1000, useFreqWeightedChiSq=False,
minProbClipForWeighting=1e-4, fidPairList=None,
comm=None, distributeMethod="gatestrings"):
rhoStrs, EStrs = pygsti.construction.get_spam_strs(myspecs)
lgstStrings = pygsti.construction.list_lgst_gatestrings(
myspecs, gsTarget.gates.keys())
lsgstStrings = pygsti.construction.make_lsgst_lists(
gsTarget.gates.keys(), rhoStrs, EStrs, mygerms, maxLs, fidPairList )
print len(myspecs[0]), " rho specifiers"
print len(myspecs[1]), " effect specifiers"
print len(mygerms), " germs"
print len(lgstStrings), " total LGST gate strings"
print len(lsgstStrings[-1]), " LSGST strings before thinning"
lsgstStringsToUse = lsgstStrings
allRequiredStrs = pygsti.remove_duplicates(lgstStrings + lsgstStrings[-1])
gs_dataGen = gsTarget.depolarize(gate_noise=0.1)
dsFake = pygsti.construction.generate_fake_data(
gs_dataGen, allRequiredStrs, nSamples, sampleError="multinomial",
seed=seed)
#Run LGST to get starting gate set
gs_lgst = pygsti.do_lgst(dsFake, myspecs, gsTarget,
svdTruncateTo=gsTarget.dim, verbosity=3)
gs_lgst_go = pygsti.optimize_gauge(gs_lgst,"target",
targetGateset=gs_dataGen)
#Run full iterative LSGST
tStart = time.time()
if obj == "chi2":
all_gs_lsgst = pygsti.do_iterative_mc2gst(
dsFake, gs_lgst_go, lsgstStringsToUse,
minProbClipForWeighting=minProbClipForWeighting,
probClipInterval=(-1e5,1e5),
verbosity=1, memLimit=3*(1024)**3, returnAll=True,
useFreqWeightedChiSq=useFreqWeightedChiSq, comm=comm,
distributeMethod=distributeMethod)
elif obj == "logl":
all_gs_lsgst = pygsti.do_iterative_mlgst(
dsFake, gs_lgst_go, lsgstStringsToUse,
minProbClip=minProbClipForWeighting,
probClipInterval=(-1e5,1e5),
verbosity=1, memLimit=3*(1024)**3, returnAll=True,
useFreqWeightedChiSq=useFreqWeightedChiSq, comm=comm,
distributeMethod=distributeMethod)
tEnd = time.time()
print "Time = ",(tEnd-tStart)/3600.0,"hours"
return all_gs_lsgst, gs_dataGen
def runAnalysis(obj, ds, myspecs, gsTarget, lsgstStringsToUse,
useFreqWeightedChiSq=False,
minProbClipForWeighting=1e-4, fidPairList=None,
comm=None, distributeMethod="gatestrings"):
#Run LGST to get starting gate set
assertGatesetsInSync(gsTarget, comm)
gs_lgst = pygsti.do_lgst(ds, myspecs, gsTarget,
svdTruncateTo=gsTarget.dim, verbosity=3)
assertGatesetsInSync(gs_lgst, comm)
gs_lgst_go = pygsti.optimize_gauge(gs_lgst,"target",
targetGateset=gsTarget)
assertGatesetsInSync(gs_lgst_go, comm)
#Run full iterative LSGST
tStart = time.time()
if obj == "chi2":
all_gs_lsgst = pygsti.do_iterative_mc2gst(
ds, gs_lgst_go, lsgstStringsToUse,
minProbClipForWeighting=minProbClipForWeighting,
probClipInterval=(-1e5,1e5),
verbosity=1, memLimit=3*(1024)**3, returnAll=True,
useFreqWeightedChiSq=useFreqWeightedChiSq, comm=comm,
distributeMethod=distributeMethod)
elif obj == "logl":
all_gs_lsgst = pygsti.do_iterative_mlgst(
ds, gs_lgst_go, lsgstStringsToUse,
minProbClip=minProbClipForWeighting,
probClipInterval=(-1e5,1e5),
verbosity=1, memLimit=3*(1024)**3, returnAll=True,
useFreqWeightedChiSq=useFreqWeightedChiSq, comm=comm,
distributeMethod=distributeMethod)
tEnd = time.time()
print("Time = ",(tEnd-tStart)/3600.0,"hours")
return all_gs_lsgst
def test_MLGST(self):
ds = self.ds
#pygsti.construction.generate_fake_data(self.datagen_gateset, self.lsgstStrings[-1],
# nSamples=1000, sampleError='binomial', seed=100)
gs_lgst = pygsti.do_lgst(ds, self.specs, self.gateset, svdTruncateTo=4, verbosity=0)
gs_lgst_go = pygsti.optimize_gauge(gs_lgst,"target",targetGateset=self.gateset, spamWeight=1.0, gateWeight=1.0)
gs_clgst = pygsti.contract(gs_lgst_go, "CPTP")
gs_single_mlgst = pygsti.do_mlgst(ds, gs_clgst, self.lsgstStrings[0], minProbClip=1e-6,
probClipInterval=(-1e2,1e2), verbosity=0)
gs_mlegst = pygsti.do_iterative_mlgst(ds, gs_clgst, self.lsgstStrings, verbosity=0,
minProbClip=1e-6, probClipInterval=(-1e2,1e2),
memLimit=1000*1024**2)
maxLogL, all_gs_mlegst_tups = pygsti.do_iterative_mlgst(
ds, gs_clgst, [ [gs.tup for gs in gsList] for gsList in self.lsgstStrings],
minProbClip=1e-6, probClipInterval=(-1e2,1e2), returnAll=True, returnMaxLogL=True)
gs_mlegst_verb = self.runSilent(pygsti.do_iterative_mlgst, ds, gs_clgst, self.lsgstStrings, verbosity=10,
minProbClip=1e-6, probClipInterval=(-1e2,1e2),
memLimit=10*1024**2)
self.assertAlmostEqual(gs_mlegst.frobeniusdist(gs_mlegst_verb),0)
self.assertAlmostEqual(gs_mlegst.frobeniusdist(all_gs_mlegst_tups[-1]),0)
#Run internal checks on less max-L values (so it doesn't take forever)
gs_mlegst_chk = pygsti.do_iterative_mlgst(ds, gs_clgst, self.lsgstStrings[0:2], verbosity=0,
minProbClip=1e-6, probClipInterval=(-1e2,1e2),
check=True)
#Other option variations - just make sure they run at this point
gs_mlegst_chk_opts = pygsti.do_iterative_mlgst(ds, gs_clgst, self.lsgstStrings[0:2], verbosity=0,
minProbClip=1e-6, probClipInterval=(-1e2,1e2),
gateStringSetLabels=["Set1","Set2"], useFreqWeightedChiSq=True )
aliased_list = [ pygsti.obj.GateString( [ (x if x != "Gx" else "GA1") for x in gs]) for gs in self.lsgstStrings[0] ]
gs_withA1 = gs_clgst.copy(); gs_withA1.gates["GA1"] = gs_clgst.gates["Gx"]
gs_mlegst_chk_opts2 = pygsti.do_mlgst(ds, gs_withA1, aliased_list, minProbClip=1e-6,
probClipInterval=(-1e2,1e2), verbosity=0,
gateLabelAliases={ 'GA1': ('Gx',) })
#Other option variations - just make sure they run at this point
gs_lsgst_chk_opts = pygsti.do_iterative_mc2gst(ds, gs_clgst, self.lsgstStrings[0:2], verbosity=0,
minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6),
useFreqWeightedChiSq=True, gateStringSetLabels=["Set1","Set2"],
gatestringWeightsDict={ ('Gx',): 2.0 } )
self.runSilent(pygsti.do_mlgst, ds, gs_clgst, self.lsgstStrings[0], minProbClip=1e-6,
probClipInterval=(-1e2,1e2), verbosity=4, memLimit=300000) #invoke memory control
pygsti.do_mlgst(ds, gs_clgst, self.lsgstStrings[0], minProbClip=1e-6,
probClipInterval=(-1e2,1e2), verbosity=0, poissonPicture=False)
#non-Poisson picture - should use (-1,-1) gateset for consistency?
#Check errors:
with self.assertRaises(MemoryError):
pygsti.do_mlgst(ds, gs_clgst, self.lsgstStrings[0], minProbClip=1e-6,
probClipInterval=(-1e2,1e2),verbosity=0, memLimit=1)
# RUN BELOW LINES TO SEED SAVED GATESET FILES
#pygsti.io.write_gateset(gs_mlegst,compare_files + "/mle_gst.gateset", "Saved MLE-GST Gateset")
gs_mle_compare = pygsti.io.load_gateset(compare_files + "/mle_gst.gateset")
gs_mlegst_go = pygsti.optimize_gauge(gs_mlegst, 'target', targetGateset=gs_mle_compare, spamWeight=1.0)
self.assertAlmostEqual( gs_mlegst_go.frobeniusdist(gs_mle_compare), 0, places=5)
def test_MC2GST(self):
ds = self.ds
#pygsti.construction.generate_fake_data(self.datagen_gateset, self.lsgstStrings[-1],
# nSamples=1000, sampleError='binomial', seed=100)
gs_lgst = pygsti.do_lgst(ds, self.specs, self.gateset, svdTruncateTo=4, verbosity=0)
gs_lgst_go = pygsti.optimize_gauge(gs_lgst,"target",targetGateset=self.gateset, spamWeight=1.0, gateWeight=1.0)
gs_clgst = pygsti.contract(gs_lgst_go, "CPTP")
gs_single_lsgst = pygsti.do_mc2gst(ds, gs_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-6,
probClipInterval=(-1e6,1e6), regularizeFactor=1e-3,
verbosity=0)
gs_lsgst = pygsti.do_iterative_mc2gst(ds, gs_clgst, self.lsgstStrings, verbosity=0,
minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6),
memLimit=1000*1024**2)
all_minErrs, all_gs_lsgst_tups = pygsti.do_iterative_mc2gst(
ds, gs_clgst, [ [gs.tup for gs in gsList] for gsList in self.lsgstStrings],
minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6), returnAll=True, returnErrorVec=True)
gs_lsgst_verb = self.runSilent(pygsti.do_iterative_mc2gst, ds, gs_clgst, self.lsgstStrings, verbosity=10,
minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6),
memLimit=10*1024**2)
gs_lsgst_reg = self.runSilent(pygsti.do_iterative_mc2gst,ds, gs_clgst,
self.lsgstStrings, verbosity=10,
minProbClipForWeighting=1e-6,
probClipInterval=(-1e6,1e6),
regularizeFactor=10, memLimit=100*1024**2)
self.assertAlmostEqual(gs_lsgst.frobeniusdist(gs_lsgst_verb),0)
self.assertAlmostEqual(gs_lsgst.frobeniusdist(all_gs_lsgst_tups[-1]),0)
#Run internal checks on less max-L values (so it doesn't take forever)
gs_lsgst_chk = pygsti.do_iterative_mc2gst(ds, gs_clgst, self.lsgstStrings[0:2], verbosity=0,
minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6),
check=True, check_jacobian=True)
gs_lsgst_chk_verb = self.runSilent(pygsti.do_iterative_mc2gst, ds, gs_clgst, self.lsgstStrings[0:2], verbosity=10,
minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6),
check=True, check_jacobian=True, memLimit=100*1024**2)
#Other option variations - just make sure they run at this point
gs_lsgst_chk_opts = pygsti.do_iterative_mc2gst(ds, gs_clgst, self.lsgstStrings[0:2], verbosity=0,
minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6),
useFreqWeightedChiSq=True, gateStringSetLabels=["Set1","Set2"],
gatestringWeightsDict={ ('Gx',): 2.0 } )
#Check with small but ok memlimit
self.runSilent(pygsti.do_mc2gst,ds, gs_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-6,
probClipInterval=(-1e6,1e6), regularizeFactor=1e-3,
verbosity=10, memLimit=300000)
#Check errors:
with self.assertRaises(MemoryError):
pygsti.do_mc2gst(ds, gs_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-6,
probClipInterval=(-1e6,1e6), regularizeFactor=1e-3,
verbosity=0, memLimit=1)
with self.assertRaises(NotImplementedError):
pygsti.do_mc2gst(ds, gs_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-6,
probClipInterval=(-1e6,1e6), regularizeFactor=1e-3,
verbosity=0, cptp_penalty_factor=1.0) #cptp pentalty not implemented yet
# RUN BELOW LINES TO SEED SAVED GATESET FILES
#pygsti.io.write_gateset(gs_lsgst,compare_files + "/lsgst.gateset", "Saved LSGST Gateset")
#pygsti.io.write_gateset(gs_lsgst_reg,compare_files + "/lsgst_reg.gateset", "Saved LSGST Gateset w/Regularization")
gs_lsgst_compare = pygsti.io.load_gateset(compare_files + "/lsgst.gateset")
gs_lsgst_reg_compare = pygsti.io.load_gateset(compare_files + "/lsgst_reg.gateset")
gs_lsgst_go = pygsti.optimize_gauge(gs_lsgst, 'target', targetGateset=gs_lsgst_compare, spamWeight=1.0)
gs_lsgst_reg_go = pygsti.optimize_gauge(gs_lsgst_reg, 'target', targetGateset=gs_lsgst_reg_compare, spamWeight=1.0)
self.assertAlmostEqual( gs_lsgst_go.frobeniusdist(gs_lsgst_compare), 0, places=5)
self.assertAlmostEqual( gs_lsgst_reg_go.frobeniusdist(gs_lsgst_reg_compare), 0, places=5)
def test_MC2GST(self):
ds = self.ds
mdl_lgst = pygsti.do_lgst(ds,
self.fiducials,
self.fiducials,
self.model,
svdTruncateTo=4,
verbosity=0)
mdl_lgst_go = pygsti.gaugeopt_to_target(mdl_lgst,
self.model, {
'spam': 1.0,
'gates': 1.0
},
checkJac=True)
mdl_clgst = pygsti.contract(mdl_lgst_go, "CPTP")
CM = profiler._get_mem_usage()
mdl_lsgst = pygsti.do_iterative_mc2gst(ds,
mdl_clgst,
self.lsgstStrings,
verbosity=0,
minProbClipForWeighting=1e-6,
probClipInterval=(-1e6, 1e6),
memLimit=CM + 1024**3)
all_minErrs, all_gs_lsgst_tups = pygsti.do_iterative_mc2gst(
ds,
mdl_clgst,
[[mdl.tup for mdl in gsList] for gsList in self.lsgstStrings],
minProbClipForWeighting=1e-6,
probClipInterval=(-1e6, 1e6),
returnAll=True,
returnErrorVec=True)
mdl_lsgst_verb = self.runSilent(pygsti.do_iterative_mc2gst,
ds,
mdl_clgst,
self.lsgstStrings,
verbosity=10,
minProbClipForWeighting=1e-6,
probClipInterval=(-1e6, 1e6),
memLimit=CM + 1024**3)
mdl_lsgst_reg = self.runSilent(pygsti.do_iterative_mc2gst,
ds,
mdl_clgst,
self.lsgstStrings,
verbosity=10,
minProbClipForWeighting=1e-6,
probClipInterval=(-1e6, 1e6),
regularizeFactor=10,
memLimit=CM + 1024**3)
self.assertAlmostEqual(mdl_lsgst.frobeniusdist(mdl_lsgst_verb), 0)
self.assertAlmostEqual(mdl_lsgst.frobeniusdist(all_gs_lsgst_tups[-1]),
0)
# RUN BELOW LINES TO SEED SAVED GATESET FILES
if regenerate_references():
pygsti.io.write_model(mdl_lsgst, compare_files + "/lsgst.model",
"Saved LSGST Model")
pygsti.io.write_model(mdl_lsgst_reg,
compare_files + "/lsgst_reg.model",
"Saved LSGST Model w/Regularization")
mdl_lsgst_compare = pygsti.io.load_model(compare_files +
"/lsgst.model")
mdl_lsgst_reg_compare = pygsti.io.load_model(compare_files +
"/lsgst_reg.model")
mdl_lsgst_go = pygsti.gaugeopt_to_target(mdl_lsgst,
mdl_lsgst_compare,
{'spam': 1.0},
checkJac=True)
mdl_lsgst_reg_go = pygsti.gaugeopt_to_target(mdl_lsgst_reg,
mdl_lsgst_reg_compare,
{'spam': 1.0},
checkJac=True)
self.assertAlmostEqual(mdl_lsgst_go.frobeniusdist(mdl_lsgst_compare),
0,
places=4)
self.assertAlmostEqual(
mdl_lsgst_reg_go.frobeniusdist(mdl_lsgst_reg_compare), 0, places=4)
# RUN BELOW LINES TO SEED SAVED GATESET FILES
if regenerate_references():
mdl_lsgst_go = pygsti.gaugeopt_to_target(mdl_lsgst, self.model,
{'spam': 1.0})
pygsti.io.write_model(mdl_lsgst_go,
compare_files + "/analysis.model",
"Saved LSGST Analysis Model")
print("DEBUG: analysis.model = ")
print(mdl_lgst_go)
def setUp(self):
super(ReportBaseCase, self).setUp()
self.targetGateset = std.gs_target
datagen_gateset = self.targetGateset.depolarize(gate_noise=0.05,
spam_noise=0.1)
self.fiducials = std.fiducials
self.germs = std.germs
self.specs = pygsti.construction.build_spam_specs(
self.fiducials, effect_labels=['E0']) #only use the first EVec
self.gateLabels = list(
self.targetGateset.gates.keys()) # also == std.gates
self.lgstStrings = pygsti.construction.list_lgst_gatestrings(
self.specs, self.gateLabels)
self.maxLengthList = [0, 1, 2, 4, 8]
self.lsgstStrings = pygsti.construction.make_lsgst_lists(
self.gateLabels, self.fiducials, self.fiducials, self.germs,
self.maxLengthList)
self.ds = pygsti.objects.DataSet(fileToLoadFrom=compare_files +
"/reportgen.dataset")
# RUN BELOW LINES TO GENERATE ANALYSIS DATASET
#ds = pygsti.construction.generate_fake_data(datagen_gateset, lsgstStrings[-1], nSamples=1000,
# sampleError='binomial', seed=100)
#ds.save(compare_files + "/reportgen.dataset")
gs_lgst = pygsti.do_lgst(self.ds,
self.specs,
self.targetGateset,
svdTruncateTo=4,
verbosity=0)
gs_lgst_go = pygsti.optimize_gauge(gs_lgst,
"target",
targetGateset=self.targetGateset,
gateWeight=1.0,
spamWeight=0.0)
self.gs_clgst = pygsti.contract(gs_lgst_go, "CPTP")
self.gs_clgst_tp = pygsti.contract(self.gs_clgst, "vSPAM")
self.gs_clgst_tp.set_all_parameterizations("TP")
try:
import pptx
self.have_python_pptx = True
except ImportError:
warnings.warn(
"**** IMPORT: Cannot import pptx (python-pptx), and so" +
" Powerpoint slide generation tests have been disabled.")
self.have_python_pptx = False
#Compute results for MC2GST
lsgst_gatesets_prego = pygsti.do_iterative_mc2gst(
self.ds,
self.gs_clgst,
self.lsgstStrings,
verbosity=0,
minProbClipForWeighting=1e-6,
probClipInterval=(-1e6, 1e6),
returnAll=True)
lsgst_gatesets = [
pygsti.optimize_gauge(gs,
"target",
targetGateset=self.targetGateset,
gateWeight=1,
spamWeight=0.001)
for gs in lsgst_gatesets_prego
]
self.results = pygsti.report.Results()
self.results.init_Ls_and_germs(
"chi2", self.targetGateset, self.ds, self.gs_clgst,
self.maxLengthList, self.germs, lsgst_gatesets, self.lsgstStrings,
self.fiducials, self.fiducials,
pygsti.construction.repeat_with_max_length, False, None,
lsgst_gatesets_prego)
self.results.parameters.update({
'minProbClip': 1e-6,
'minProbClipForWeighting': 1e-4,
'probClipInterval': (-1e6, 1e6),
'radius': 1e-4,
'weights': None,
'defaultDirectory': temp_files + "",
'defaultBasename': "MyDefaultReportName"
})
self.results.options.precision = 4
self.results.options.polar_precision = 3
#Compute results for MLGST with TP constraint
lsgst_gatesets_TP = pygsti.do_iterative_mlgst(self.ds,
self.gs_clgst_tp,
self.lsgstStrings,
verbosity=0,
minProbClip=1e-4,
probClipInterval=(-1e6,
1e6),
returnAll=True)
lsgst_gatesets_TP = [
pygsti.optimize_gauge(gs,
"target",
targetGateset=self.targetGateset,
constrainToTP=True,
gateWeight=1,
spamWeight=0.001) for gs in lsgst_gatesets_TP
]
self.results_logL = pygsti.report.Results()
self.results_logL.init_Ls_and_germs(
"logl", self.targetGateset, self.ds, self.gs_clgst_tp,
self.maxLengthList, self.germs, lsgst_gatesets_TP,
self.lsgstStrings, self.fiducials, self.fiducials,
pygsti.construction.repeat_with_max_length, True)
self.results_logL.options.precision = 4
self.results_logL.options.polar_precision = 3
try:
basestring #Only defined in Python 2
self.versionsuffix = "" #Python 2
except NameError:
self.versionsuffix = "v3" #Python 3
def test_reports_chi2(self):
lsgst_gatesets_prego = pygsti.do_iterative_mc2gst(self.ds, self.gs_clgst, self.lsgstStrings, verbosity=0,
minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6),
returnAll=True)
lsgst_gatesets = [ pygsti.optimize_gauge(gs, "target", targetGateset=self.targetGateset,
gateWeight=1,spamWeight=0.001) for gs in lsgst_gatesets_prego]
self.results = pygsti.report.Results()
self.results.init_Ls_and_germs("chi2", self.targetGateset, self.ds, self.gs_clgst,
self.maxLengthList, self.germs,
lsgst_gatesets, self.lsgstStrings, self.fiducials, self.fiducials,
pygsti.construction.repeat_with_max_length, False, None, lsgst_gatesets_prego)
self.results.parameters.update({'minProbClip': 1e-6, 'minProbClipForWeighting': 1e-4,
'probClipInterval': (-1e6,1e6), 'radius': 1e-4,
'weights': None, 'defaultDirectory': "temp_test_files",
'defaultBasename': "MyDefaultReportName" } )
#db = lsgst_gatesets[-1]
#firstElIdentityVec = np.zeros( (db.dim,1) )
#firstElIdentityVec[0] = db.dim**0.25
#db.povm_identity = firstElIdentityVec
#
#print "Target Identity = ", np.asarray(self.targetGateset.povm_identity)
#print "Identity = ", np.asarray(db.povm_identity)
#print "rho0 = ", np.asarray(db.preps['rho0'])
#for plbl in db.get_prep_labels():
# for elbl in db.get_effect_labels():
# print "DB: dot(%s,%s) = " % (plbl,elbl), np.dot(np.transpose(db.effects[elbl]),db.preps[plbl])
#return
self.results.create_full_report_pdf(filename="temp_test_files/full_reportA.pdf", confidenceLevel=None,
debugAidsAppendix=False, gaugeOptAppendix=False,
pixelPlotAppendix=False, whackamoleAppendix=False)
self.results.create_brief_report_pdf(filename="temp_test_files/brief_reportA.pdf", confidenceLevel=None)
self.results.create_presentation_pdf(filename="temp_test_files/slidesA.pdf", confidenceLevel=None,
debugAidsAppendix=False, pixelPlotAppendix=False, whackamoleAppendix=False)
if self.have_python_pptx:
self.results.create_presentation_ppt(filename="temp_test_files/slidesA.ppt", confidenceLevel=None,
debugAidsAppendix=False, pixelPlotAppendix=False, whackamoleAppendix=False)
#Run again using default filenames
self.results.create_full_report_pdf(filename="auto", confidenceLevel=None,
debugAidsAppendix=False, gaugeOptAppendix=False,
pixelPlotAppendix=False, whackamoleAppendix=False)
self.results.create_brief_report_pdf(filename="auto", confidenceLevel=None)
self.results.create_presentation_pdf(filename="auto", confidenceLevel=None,
debugAidsAppendix=False, pixelPlotAppendix=False, whackamoleAppendix=False)
if self.have_python_pptx:
self.results.create_presentation_ppt(filename="auto", confidenceLevel=None,
debugAidsAppendix=False, pixelPlotAppendix=False, whackamoleAppendix=False)
#Compare the text files, assume if these match the PDFs are equivalent
self.checkFile("full_reportA.tex")
self.checkFile("brief_reportA.tex")
self.checkFile("slidesA.tex")
self.results.create_full_report_pdf(filename="temp_test_files/full_reportB.pdf", confidenceLevel=95,
debugAidsAppendix=True, gaugeOptAppendix=True,
pixelPlotAppendix=True, whackamoleAppendix=True,
verbosity=2)
self.results.create_full_report_pdf(filename="temp_test_files/full_reportB-noGOpt.pdf", confidenceLevel=95,
debugAidsAppendix=True, gaugeOptAppendix=False,
pixelPlotAppendix=True, whackamoleAppendix=True) # to test blank GOpt tables
self.results.create_brief_report_pdf(filename="temp_test_files/brief_reportB.pdf", confidenceLevel=95, verbosity=2)
self.results.create_presentation_pdf(filename="temp_test_files/slidesB.pdf", confidenceLevel=95,
debugAidsAppendix=True, pixelPlotAppendix=True, whackamoleAppendix=True,
verbosity=2)
if self.have_python_pptx:
self.results.create_presentation_ppt(filename="temp_test_files/slidesB.ppt", confidenceLevel=95,
debugAidsAppendix=True, pixelPlotAppendix=True, whackamoleAppendix=True,
verbosity=2)
#Compare the text files, assume if these match the PDFs are equivalent
self.checkFile("full_reportB.tex")
self.checkFile("full_reportB_appendices.tex")
self.checkFile("brief_reportB.tex")
self.checkFile("slidesB.tex")
#Non-markovian error bars (negative confidenceLevel) & tooltips
self.results.create_full_report_pdf(filename="temp_test_files/full_reportE.pdf", confidenceLevel=-95,
debugAidsAppendix=True, gaugeOptAppendix=True,
pixelPlotAppendix=True, whackamoleAppendix=True,
verbosity=2, tips=True)
self.results.create_brief_report_pdf(filename="temp_test_files/brief_reportE.pdf", confidenceLevel=-95,
verbosity=2, tips=True)
#Compare the text files, assume if these match the PDFs are equivalent
self.checkFile("full_reportE.tex")
self.checkFile("full_reportE_appendices.tex")
def setUp(self):
#Set GateSet objects to "strict" mode for testing
pygsti.objects.GateSet._strict = True
self.targetGateset = std.gs_target
datagen_gateset = self.targetGateset.depolarize(gate_noise=0.05, spam_noise=0.1)
self.fiducials = std.fiducials
self.germs = std.germs
self.specs = pygsti.construction.build_spam_specs(self.fiducials, effect_labels=['E0']) #only use the first EVec
self.gateLabels = self.targetGateset.gates.keys() # also == std.gates
self.lgstStrings = pygsti.construction.list_lgst_gatestrings(self.specs, self.gateLabels)
self.maxLengthList = [0,1,2,4,8]
self.lsgstStrings = pygsti.construction.make_lsgst_lists(
self.gateLabels, self.fiducials, self.fiducials, self.germs, self.maxLengthList)
self.ds = pygsti.objects.DataSet(fileToLoadFrom="cmp_chk_files/reportgen.dataset")
# RUN BELOW LINES TO GENERATE ANALYSIS DATASET
#ds = pygsti.construction.generate_fake_data(datagen_gateset, lsgstStrings[-1], nSamples=1000,
# sampleError='binomial', seed=100)
#ds.save("cmp_chk_files/reportgen.dataset")
gs_lgst = pygsti.do_lgst(self.ds, self.specs, self.targetGateset, svdTruncateTo=4, verbosity=0)
gs_lgst_go = pygsti.optimize_gauge(gs_lgst,"target",targetGateset=self.targetGateset)
self.gs_clgst = pygsti.contract(gs_lgst_go, "CPTP")
self.gs_clgst_tp = pygsti.contract(self.gs_clgst, "vSPAM")
self.gs_clgst_tp.set_all_parameterizations("TP")
try:
import pptx
self.have_python_pptx = True
except ImportError:
warnings.warn("**** IMPORT: Cannot import pptx (python-pptx), and so" +
" Powerpoint slide generation tests have been disabled.")
self.have_python_pptx = False
#Compute results for MC2GST
lsgst_gatesets_prego = pygsti.do_iterative_mc2gst(self.ds, self.gs_clgst, self.lsgstStrings, verbosity=0,
minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6),
returnAll=True)
lsgst_gatesets = [ pygsti.optimize_gauge(gs, "target", targetGateset=self.targetGateset,
gateWeight=1,spamWeight=0.001) for gs in lsgst_gatesets_prego]
self.results = pygsti.report.Results()
self.results.init_Ls_and_germs("chi2", self.targetGateset, self.ds, self.gs_clgst,
self.maxLengthList, self.germs,
lsgst_gatesets, self.lsgstStrings, self.fiducials, self.fiducials,
pygsti.construction.repeat_with_max_length, False, None, lsgst_gatesets_prego)
self.results.parameters.update({'minProbClip': 1e-6, 'minProbClipForWeighting': 1e-4,
'probClipInterval': (-1e6,1e6), 'radius': 1e-4,
'weights': None, 'defaultDirectory': "temp_test_files",
'defaultBasename': "MyDefaultReportName" } )
#Compute results for MLGST with TP constraint
lsgst_gatesets_TP = pygsti.do_iterative_mlgst(self.ds, self.gs_clgst_tp, self.lsgstStrings, verbosity=0,
minProbClip=1e-4, probClipInterval=(-1e6,1e6),
returnAll=True)
lsgst_gatesets_TP = [ pygsti.optimize_gauge(gs, "target", targetGateset=self.targetGateset, constrainToTP=True,
gateWeight=1,spamWeight=0.001) for gs in lsgst_gatesets_TP]
self.results_logL = pygsti.report.Results()
self.results_logL.init_Ls_and_germs("logl", self.targetGateset, self.ds, self.gs_clgst_tp, self.maxLengthList, self.germs,
lsgst_gatesets_TP, self.lsgstStrings, self.fiducials, self.fiducials,
pygsti.construction.repeat_with_max_length, True)
def test_MC2GST(self):
ds = self.ds
#pygsti.construction.generate_fake_data(self.datagen_gateset, self.lsgstStrings[-1],
# nSamples=1000, sampleError='binomial', seed=100)
mdl_lgst = pygsti.do_lgst(ds, self.fiducials, self.fiducials, self.model, svdTruncateTo=4, verbosity=0)
mdl_lgst_go = pygsti.gaugeopt_to_target(mdl_lgst,self.model, {'spam':1.0, 'gates': 1.0}, checkJac=True)
mdl_clgst = pygsti.contract(mdl_lgst_go, "CPTP")
CM = pygsti.baseobjs.profiler._get_mem_usage()
mdl_single_lsgst = pygsti.do_mc2gst(ds, mdl_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-4,
probClipInterval=(-1e6,1e6), regularizeFactor=1e-3,
verbosity=0) #uses regularizeFactor
mdl_single_lsgst_cp = pygsti.do_mc2gst(ds, mdl_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-4,
probClipInterval=(-1e6,1e6), cptp_penalty_factor=1.0,
verbosity=0) #uses cptp_penalty_factor
mdl_single_lsgst_sp = pygsti.do_mc2gst(ds, mdl_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-4,
probClipInterval=(-1e6,1e6), spam_penalty_factor=1.0,
verbosity=0) #uses spam_penalty_factor
mdl_single_lsgst_cpsp = pygsti.do_mc2gst(ds, mdl_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-4,
probClipInterval=(-1e6,1e6), cptp_penalty_factor=1.0,
spam_penalty_factor=1.0, verbosity=0) #uses both penalty factors
mdl_single_lsgst_cpsp = self.runSilent(pygsti.do_mc2gst, ds, mdl_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-4,
probClipInterval=(-1e6,1e6), cptp_penalty_factor=1.0,
spam_penalty_factor=1.0, verbosity=10) #uses both penalty factors w/verbosity high
mdl_single_lsgst_cp = self.runSilent(pygsti.do_mc2gst, ds, mdl_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-4,
probClipInterval=(-1e6,1e6), cptp_penalty_factor=1.0,
verbosity=10) #uses cptp_penalty_factor w/verbosity high
mdl_single_lsgst_sp = self.runSilent(pygsti.do_mc2gst, ds, mdl_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-4,
probClipInterval=(-1e6,1e6), spam_penalty_factor=1.0,
verbosity=10) #uses spam_penalty_factor w/verbosity high
mdl_lsgst = pygsti.do_iterative_mc2gst(ds, mdl_clgst, self.lsgstStrings, verbosity=0,
minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6),
memLimit=CM + 1024**3)
all_minErrs, all_gs_lsgst_tups = pygsti.do_iterative_mc2gst(
ds, mdl_clgst, [ [mdl.tup for mdl in gsList] for gsList in self.lsgstStrings],
minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6), returnAll=True, returnErrorVec=True)
mdl_lsgst_verb = self.runSilent(pygsti.do_iterative_mc2gst, ds, mdl_clgst, self.lsgstStrings, verbosity=10,
minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6),
memLimit=CM + 1024**3)
mdl_lsgst_reg = self.runSilent(pygsti.do_iterative_mc2gst,ds, mdl_clgst,
self.lsgstStrings, verbosity=10,
minProbClipForWeighting=1e-6,
probClipInterval=(-1e6,1e6),
regularizeFactor=10, memLimit=CM + 1024**3)
self.assertAlmostEqual(mdl_lsgst.frobeniusdist(mdl_lsgst_verb),0)
self.assertAlmostEqual(mdl_lsgst.frobeniusdist(all_gs_lsgst_tups[-1]),0)
#Run internal checks on less max-L values (so it doesn't take forever)
mdl_lsgst_chk = pygsti.do_iterative_mc2gst(ds, mdl_clgst, self.lsgstStrings[0:2], verbosity=0,
minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6),
check=True, check_jacobian=True)
mdl_lsgst_chk_verb = self.runSilent(pygsti.do_iterative_mc2gst, ds, mdl_clgst, self.lsgstStrings[0:2], verbosity=10,
minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6),
check=True, check_jacobian=True, memLimit=CM + 1024**3)
#Other option variations - just make sure they run at this point
mdl_lsgst_chk_opts = pygsti.do_iterative_mc2gst(ds, mdl_clgst, self.lsgstStrings[0:2], verbosity=0,
minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6),
useFreqWeightedChiSq=True, circuitSetLabels=["Set1","Set2"],
circuitWeightsDict={ ('Gx',): 2.0 } )
aliased_list = [ pygsti.obj.Circuit( [ (x if x != L("Gx") else L("GA1")) for x in mdl]) for mdl in self.lsgstStrings[0] ]
mdl_withA1 = mdl_clgst.copy(); mdl_withA1.operations["GA1"] = mdl_clgst.operations["Gx"]
del mdl_withA1.operations["Gx"] # otherwise mdl_withA1 will have Gx params that we have no knowledge of!
mdl_lsgst_chk_opts2 = pygsti.do_mc2gst(ds, mdl_withA1, aliased_list, minProbClipForWeighting=1e-6,
probClipInterval=(-1e2,1e2), verbosity=10,
opLabelAliases={ L('GA1'): (L('Gx'),) })
#Check with small but ok memlimit -- not anymore since new mem estimation uses current memory, making this non-robust
#self.runSilent(pygsti.do_mc2gst,ds, mdl_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-6,
# probClipInterval=(-1e6,1e6), regularizeFactor=1e-3,
# verbosity=10, memLimit=CM + 1024**3)
#Check errors:
with self.assertRaises(MemoryError):
pygsti.do_mc2gst(ds, mdl_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-6,
probClipInterval=(-1e6,1e6), regularizeFactor=1e-3,
verbosity=0, memLimit=1)
with self.assertRaises(AssertionError):
pygsti.do_mc2gst(ds, mdl_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-6,
probClipInterval=(-1e6,1e6), regularizeFactor=1e-3,
verbosity=0, cptp_penalty_factor=1.0) #can't specify both cptp_penalty_factor and regularizeFactor
# RUN BELOW LINES TO SEED SAVED GATESET FILES
if os.environ.get('PYGSTI_REGEN_REF_FILES','no').lower() in ("yes","1","true"):
pygsti.io.write_model(mdl_lsgst,compare_files + "/lsgst.model", "Saved LSGST Model")
pygsti.io.write_model(mdl_lsgst_reg,compare_files + "/lsgst_reg.model", "Saved LSGST Model w/Regularization")
mdl_lsgst_compare = pygsti.io.load_model(compare_files + "/lsgst.model")
mdl_lsgst_reg_compare = pygsti.io.load_model(compare_files + "/lsgst_reg.model")
mdl_lsgst_go = pygsti.gaugeopt_to_target(mdl_lsgst, mdl_lsgst_compare, {'spam':1.0}, checkJac=True)
mdl_lsgst_reg_go = pygsti.gaugeopt_to_target(mdl_lsgst_reg, mdl_lsgst_reg_compare, {'spam':1.0}, checkJac=True)
self.assertAlmostEqual( mdl_lsgst_go.frobeniusdist(mdl_lsgst_compare), 0, places=4)
self.assertAlmostEqual( mdl_lsgst_reg_go.frobeniusdist(mdl_lsgst_reg_compare), 0, places=4)
# RUN BELOW LINES TO SEED SAVED GATESET FILES
if os.environ.get('PYGSTI_REGEN_REF_FILES','no').lower() in ("yes","1","true"):
mdl_lsgst_go = pygsti.gaugeopt_to_target(mdl_lsgst, self.model, {'spam':1.0})
pygsti.io.write_model(mdl_lsgst_go,compare_files + "/analysis.model", "Saved LSGST Analysis Model")
print("DEBUG: analysis.model = "); print(mdl_lgst_go)
def test_MC2GST(self):
ds = self.ds
#pygsti.construction.generate_fake_data(self.datagen_gateset, self.lsgstStrings[-1],
# nSamples=1000, sampleError='binomial', seed=100)
gs_lgst = pygsti.do_lgst(ds, self.fiducials, self.fiducials, self.gateset, svdTruncateTo=4, verbosity=0)
gs_lgst_go = pygsti.gaugeopt_to_target(gs_lgst,self.gateset, {'spam':1.0, 'gates': 1.0}, checkJac=True)
gs_clgst = pygsti.contract(gs_lgst_go, "CPTP")
CM = pygsti.baseobjs.profiler._get_mem_usage()
gs_single_lsgst = pygsti.do_mc2gst(ds, gs_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-4,
probClipInterval=(-1e6,1e6), regularizeFactor=1e-3,
verbosity=0) #uses regularizeFactor
gs_single_lsgst_cp = pygsti.do_mc2gst(ds, gs_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-4,
probClipInterval=(-1e6,1e6), cptp_penalty_factor=1.0,
verbosity=0) #uses cptp_penalty_factor
gs_single_lsgst_sp = pygsti.do_mc2gst(ds, gs_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-4,
probClipInterval=(-1e6,1e6), spam_penalty_factor=1.0,
verbosity=0) #uses spam_penalty_factor
gs_single_lsgst_cpsp = pygsti.do_mc2gst(ds, gs_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-4,
probClipInterval=(-1e6,1e6), cptp_penalty_factor=1.0,
spam_penalty_factor=1.0, verbosity=0) #uses both penalty factors
gs_single_lsgst_cpsp = self.runSilent(pygsti.do_mc2gst, ds, gs_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-4,
probClipInterval=(-1e6,1e6), cptp_penalty_factor=1.0,
spam_penalty_factor=1.0, verbosity=10) #uses both penalty factors w/verbosity high
gs_single_lsgst_cp = self.runSilent(pygsti.do_mc2gst, ds, gs_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-4,
probClipInterval=(-1e6,1e6), cptp_penalty_factor=1.0,
verbosity=10) #uses cptp_penalty_factor w/verbosity high
gs_single_lsgst_sp = self.runSilent(pygsti.do_mc2gst, ds, gs_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-4,
probClipInterval=(-1e6,1e6), spam_penalty_factor=1.0,
verbosity=10) #uses spam_penalty_factor w/verbosity high
gs_lsgst = pygsti.do_iterative_mc2gst(ds, gs_clgst, self.lsgstStrings, verbosity=0,
minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6),
memLimit=CM + 1024**3)
all_minErrs, all_gs_lsgst_tups = pygsti.do_iterative_mc2gst(
ds, gs_clgst, [ [gs.tup for gs in gsList] for gsList in self.lsgstStrings],
minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6), returnAll=True, returnErrorVec=True)
gs_lsgst_verb = self.runSilent(pygsti.do_iterative_mc2gst, ds, gs_clgst, self.lsgstStrings, verbosity=10,
minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6),
memLimit=CM + 1024**3)
gs_lsgst_reg = self.runSilent(pygsti.do_iterative_mc2gst,ds, gs_clgst,
self.lsgstStrings, verbosity=10,
minProbClipForWeighting=1e-6,
probClipInterval=(-1e6,1e6),
regularizeFactor=10, memLimit=CM + 1024**3)
self.assertAlmostEqual(gs_lsgst.frobeniusdist(gs_lsgst_verb),0)
self.assertAlmostEqual(gs_lsgst.frobeniusdist(all_gs_lsgst_tups[-1]),0)
#Run internal checks on less max-L values (so it doesn't take forever)
gs_lsgst_chk = pygsti.do_iterative_mc2gst(ds, gs_clgst, self.lsgstStrings[0:2], verbosity=0,
minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6),
check=True, check_jacobian=True)
gs_lsgst_chk_verb = self.runSilent(pygsti.do_iterative_mc2gst, ds, gs_clgst, self.lsgstStrings[0:2], verbosity=10,
minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6),
check=True, check_jacobian=True, memLimit=CM + 1024**3)
#Other option variations - just make sure they run at this point
gs_lsgst_chk_opts = pygsti.do_iterative_mc2gst(ds, gs_clgst, self.lsgstStrings[0:2], verbosity=0,
minProbClipForWeighting=1e-6, probClipInterval=(-1e6,1e6),
useFreqWeightedChiSq=True, gateStringSetLabels=["Set1","Set2"],
gatestringWeightsDict={ ('Gx',): 2.0 } )
aliased_list = [ pygsti.obj.GateString( [ (x if x != "Gx" else "GA1") for x in gs]) for gs in self.lsgstStrings[0] ]
gs_withA1 = gs_clgst.copy(); gs_withA1.gates["GA1"] = gs_clgst.gates["Gx"]
del gs_withA1.gates["Gx"] # otherwise gs_withA1 will have Gx params that we have no knowledge of!
gs_lsgst_chk_opts2 = pygsti.do_mc2gst(ds, gs_withA1, aliased_list, minProbClipForWeighting=1e-6,
probClipInterval=(-1e2,1e2), verbosity=10,
gateLabelAliases={ 'GA1': ('Gx',) })
#Check with small but ok memlimit -- not anymore since new mem estimation uses current memory, making this non-robust
#self.runSilent(pygsti.do_mc2gst,ds, gs_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-6,
# probClipInterval=(-1e6,1e6), regularizeFactor=1e-3,
# verbosity=10, memLimit=CM + 1024**3)
#Check errors:
with self.assertRaises(MemoryError):
pygsti.do_mc2gst(ds, gs_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-6,
probClipInterval=(-1e6,1e6), regularizeFactor=1e-3,
verbosity=0, memLimit=1)
with self.assertRaises(AssertionError):
pygsti.do_mc2gst(ds, gs_clgst, self.lsgstStrings[0], minProbClipForWeighting=1e-6,
probClipInterval=(-1e6,1e6), regularizeFactor=1e-3,
verbosity=0, cptp_penalty_factor=1.0) #can't specify both cptp_penalty_factor and regularizeFactor
# RUN BELOW LINES TO SEED SAVED GATESET FILES
#pygsti.io.write_gateset(gs_lsgst,compare_files + "/lsgst.gateset", "Saved LSGST Gateset")
#pygsti.io.write_gateset(gs_lsgst_reg,compare_files + "/lsgst_reg.gateset", "Saved LSGST Gateset w/Regularization")
gs_lsgst_compare = pygsti.io.load_gateset(compare_files + "/lsgst.gateset")
gs_lsgst_reg_compare = pygsti.io.load_gateset(compare_files + "/lsgst_reg.gateset")
gs_lsgst_go = pygsti.gaugeopt_to_target(gs_lsgst, gs_lsgst_compare, {'spam':1.0}, checkJac=True)
gs_lsgst_reg_go = pygsti.gaugeopt_to_target(gs_lsgst_reg, gs_lsgst_reg_compare, {'spam':1.0}, checkJac=True)
self.assertAlmostEqual( gs_lsgst_go.frobeniusdist(gs_lsgst_compare), 0, places=4)
self.assertAlmostEqual( gs_lsgst_reg_go.frobeniusdist(gs_lsgst_reg_compare), 0, places=4)
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
targetGateset = std.gs_target
datagen_gateset = targetGateset.depolarize(gate_noise=0.05,
spam_noise=0.1)
datagen_gateset2 = targetGateset.depolarize(gate_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
gateLabels = std.gates
cls.lgstStrings = pygsti.construction.list_lgst_gatestrings(
std.fiducials, std.fiducials, gateLabels)
cls.maxLengthList = [1, 2, 4, 8]
cls.lsgstStrings = pygsti.construction.make_lsgst_lists(
gateLabels, std.fiducials, std.fiducials, std.germs,
cls.maxLengthList)
cls.lsgstStructs = pygsti.construction.make_lsgst_structs(
gateLabels, std.fiducials, std.fiducials, std.germs,
cls.maxLengthList)
try:
basestring #Only defined in Python 2
cls.versionsuffix = "" #Python 2
except NameError:
cls.versionsuffix = "v3" #Python 3
# RUN BELOW LINES TO GENERATE ANALYSIS DATASET
#ds = pygsti.construction.generate_fake_data(datagen_gateset, cls.lsgstStrings[-1], nSamples=1000,
# sampleError='binomial', seed=100)
#ds.save(compare_files + "/reportgen.dataset%s" % cls.versionsuffix)
#ds2 = pygsti.construction.generate_fake_data(datagen_gateset2, cls.lsgstStrings[-1], nSamples=1000,
# sampleError='binomial', seed=100)
#ds2.save(compare_files + "/reportgen2.dataset%s" % cls.versionsuffix)
cls.ds = pygsti.objects.DataSet(
fileToLoadFrom=compare_files +
"/reportgen.dataset%s" % cls.versionsuffix)
cls.ds2 = pygsti.objects.DataSet(
fileToLoadFrom=compare_files +
"/reportgen2.dataset%s" % cls.versionsuffix)
gs_lgst = pygsti.do_lgst(cls.ds,
std.fiducials,
std.fiducials,
targetGateset,
svdTruncateTo=4,
verbosity=0)
gs_lgst_go = pygsti.gaugeopt_to_target(gs_lgst, targetGateset, {
'gates': 1.0,
'spam': 0.0
})
cls.gs_clgst = pygsti.contract(gs_lgst_go, "CPTP")
cls.gs_clgst_tp = pygsti.contract(cls.gs_clgst, "vSPAM")
cls.gs_clgst_tp.set_all_parameterizations("TP")
#Compute results for MC2GST
lsgst_gatesets_prego = pygsti.do_iterative_mc2gst(
cls.ds,
cls.gs_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_gatestrings(cls.lsgstStructs)
cls.results.add_estimate(
targetGateset, cls.gs_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([
('gateset', lsgst_gatesets_prego[-1]), #so can gauge-propagate CIs
('targetGateset', targetGateset), #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 = targetGateset.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.gs_clgst_tp, cls.lsgstStrings, verbosity=0,
# minProbClip=1e-4, probClipInterval=(-1e6,1e6),
# returnAll=True) #TP initial gateset => TP output gatesets
#cls.results_logL = pygsti.objects.Results()
#cls.results_logL.init_dataset(cls.ds)
#cls.results_logL.init_gatestrings(cls.lsgstStructs)
#cls.results_logL.add_estimate(targetGateset, cls.gs_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 = targetGateset.copy(); tp_target.set_all_parameterizations("TP")
#gaugeOptParams = gaugeOptParams.copy() #just to be safe
#gaugeOptParams['gateset'] = lsgst_gatesets_TP[-1] #so can gauge-propagate CIs
#gaugeOptParams['targetGateset'] = 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 test_MLGST(self):
ds = self.ds
#pygsti.construction.generate_fake_data(self.datagen_gateset, self.lsgstStrings[-1],
# nSamples=1000, sampleError='binomial', seed=100)
gs_lgst = pygsti.do_lgst(ds,
self.specs,
self.gateset,
svdTruncateTo=4,
verbosity=0)
gs_lgst_go = pygsti.optimize_gauge(gs_lgst,
"target",
targetGateset=self.gateset,
spamWeight=1.0,
gateWeight=1.0)
gs_clgst = pygsti.contract(gs_lgst_go, "CPTP")
gs_single_mlgst = pygsti.do_mlgst(ds,
gs_clgst,
self.lsgstStrings[0],
minProbClip=1e-6,
probClipInterval=(-1e2, 1e2),
verbosity=0)
gs_mlegst = pygsti.do_iterative_mlgst(ds,
gs_clgst,
self.lsgstStrings,
verbosity=0,
minProbClip=1e-6,
probClipInterval=(-1e2, 1e2),
memLimit=1000 * 1024**2)
maxLogL, all_gs_mlegst_tups = pygsti.do_iterative_mlgst(
ds,
gs_clgst,
[[gs.tup for gs in gsList] for gsList in self.lsgstStrings],
minProbClip=1e-6,
probClipInterval=(-1e2, 1e2),
returnAll=True,
returnMaxLogL=True)
gs_mlegst_verb = self.runSilent(pygsti.do_iterative_mlgst,
ds,
gs_clgst,
self.lsgstStrings,
verbosity=10,
minProbClip=1e-6,
probClipInterval=(-1e2, 1e2),
memLimit=10 * 1024**2)
self.assertAlmostEqual(gs_mlegst.frobeniusdist(gs_mlegst_verb), 0)
self.assertAlmostEqual(gs_mlegst.frobeniusdist(all_gs_mlegst_tups[-1]),
0)
#Run internal checks on less max-L values (so it doesn't take forever)
gs_mlegst_chk = pygsti.do_iterative_mlgst(ds,
gs_clgst,
self.lsgstStrings[0:2],
verbosity=0,
minProbClip=1e-6,
probClipInterval=(-1e2, 1e2),
check=True)
#Other option variations - just make sure they run at this point
gs_mlegst_chk_opts = pygsti.do_iterative_mlgst(
ds,
gs_clgst,
self.lsgstStrings[0:2],
verbosity=0,
minProbClip=1e-6,
probClipInterval=(-1e2, 1e2),
gateStringSetLabels=["Set1", "Set2"],
useFreqWeightedChiSq=True)
aliased_list = [
pygsti.obj.GateString([(x if x != "Gx" else "GA1") for x in gs])
for gs in self.lsgstStrings[0]
]
gs_withA1 = gs_clgst.copy()
gs_withA1.gates["GA1"] = gs_clgst.gates["Gx"]
gs_mlegst_chk_opts2 = pygsti.do_mlgst(
ds,
gs_withA1,
aliased_list,
minProbClip=1e-6,
probClipInterval=(-1e2, 1e2),
verbosity=0,
gateLabelAliases={'GA1': ('Gx', )})
#Other option variations - just make sure they run at this point
gs_lsgst_chk_opts = pygsti.do_iterative_mc2gst(
ds,
gs_clgst,
self.lsgstStrings[0:2],
verbosity=0,
minProbClipForWeighting=1e-6,
probClipInterval=(-1e6, 1e6),
useFreqWeightedChiSq=True,
gateStringSetLabels=["Set1", "Set2"],
gatestringWeightsDict={('Gx', ): 2.0})
self.runSilent(pygsti.do_mlgst,
ds,
gs_clgst,
self.lsgstStrings[0],
minProbClip=1e-6,
probClipInterval=(-1e2, 1e2),
verbosity=4,
memLimit=300000) #invoke memory control
pygsti.do_mlgst(ds,
gs_clgst,
self.lsgstStrings[0],
minProbClip=1e-6,
probClipInterval=(-1e2, 1e2),
verbosity=0,
poissonPicture=False)
#non-Poisson picture - should use (-1,-1) gateset for consistency?
#Check errors:
with self.assertRaises(MemoryError):
pygsti.do_mlgst(ds,
gs_clgst,
self.lsgstStrings[0],
minProbClip=1e-6,
probClipInterval=(-1e2, 1e2),
verbosity=0,
memLimit=1)
# RUN BELOW LINES TO SEED SAVED GATESET FILES
#pygsti.io.write_gateset(gs_mlegst,compare_files + "/mle_gst.gateset", "Saved MLE-GST Gateset")
gs_mle_compare = pygsti.io.load_gateset(compare_files +
"/mle_gst.gateset")
gs_mlegst_go = pygsti.optimize_gauge(gs_mlegst,
'target',
targetGateset=gs_mle_compare,
spamWeight=1.0)
self.assertAlmostEqual(gs_mlegst_go.frobeniusdist(gs_mle_compare),
0,
places=5)
def test_MC2GST(self):
ds = self.ds
#pygsti.construction.generate_fake_data(self.datagen_gateset, self.lsgstStrings[-1],
# nSamples=1000, sampleError='binomial', seed=100)
gs_lgst = pygsti.do_lgst(ds,
self.specs,
self.gateset,
svdTruncateTo=4,
verbosity=0)
gs_lgst_go = pygsti.optimize_gauge(gs_lgst,
"target",
targetGateset=self.gateset,
spamWeight=1.0,
gateWeight=1.0)
gs_clgst = pygsti.contract(gs_lgst_go, "CPTP")
gs_single_lsgst = pygsti.do_mc2gst(ds,
gs_clgst,
self.lsgstStrings[0],
minProbClipForWeighting=1e-6,
probClipInterval=(-1e6, 1e6),
regularizeFactor=1e-3,
verbosity=0)
gs_lsgst = pygsti.do_iterative_mc2gst(ds,
gs_clgst,
self.lsgstStrings,
verbosity=0,
minProbClipForWeighting=1e-6,
probClipInterval=(-1e6, 1e6),
memLimit=1000 * 1024**2)
all_minErrs, all_gs_lsgst_tups = pygsti.do_iterative_mc2gst(
ds,
gs_clgst,
[[gs.tup for gs in gsList] for gsList in self.lsgstStrings],
minProbClipForWeighting=1e-6,
probClipInterval=(-1e6, 1e6),
returnAll=True,
returnErrorVec=True)
gs_lsgst_verb = self.runSilent(pygsti.do_iterative_mc2gst,
ds,
gs_clgst,
self.lsgstStrings,
verbosity=10,
minProbClipForWeighting=1e-6,
probClipInterval=(-1e6, 1e6),
memLimit=10 * 1024**2)
gs_lsgst_reg = self.runSilent(pygsti.do_iterative_mc2gst,
ds,
gs_clgst,
self.lsgstStrings,
verbosity=10,
minProbClipForWeighting=1e-6,
probClipInterval=(-1e6, 1e6),
regularizeFactor=10,
memLimit=100 * 1024**2)
self.assertAlmostEqual(gs_lsgst.frobeniusdist(gs_lsgst_verb), 0)
self.assertAlmostEqual(gs_lsgst.frobeniusdist(all_gs_lsgst_tups[-1]),
0)
#Run internal checks on less max-L values (so it doesn't take forever)
gs_lsgst_chk = pygsti.do_iterative_mc2gst(ds,
gs_clgst,
self.lsgstStrings[0:2],
verbosity=0,
minProbClipForWeighting=1e-6,
probClipInterval=(-1e6, 1e6),
check=True,
check_jacobian=True)
gs_lsgst_chk_verb = self.runSilent(pygsti.do_iterative_mc2gst,
ds,
gs_clgst,
self.lsgstStrings[0:2],
verbosity=10,
minProbClipForWeighting=1e-6,
probClipInterval=(-1e6, 1e6),
check=True,
check_jacobian=True,
memLimit=100 * 1024**2)
#Other option variations - just make sure they run at this point
gs_lsgst_chk_opts = pygsti.do_iterative_mc2gst(
ds,
gs_clgst,
self.lsgstStrings[0:2],
verbosity=0,
minProbClipForWeighting=1e-6,
probClipInterval=(-1e6, 1e6),
useFreqWeightedChiSq=True,
gateStringSetLabels=["Set1", "Set2"],
gatestringWeightsDict={('Gx', ): 2.0})
#Check with small but ok memlimit
self.runSilent(pygsti.do_mc2gst,
ds,
gs_clgst,
self.lsgstStrings[0],
minProbClipForWeighting=1e-6,
probClipInterval=(-1e6, 1e6),
regularizeFactor=1e-3,
verbosity=10,
memLimit=300000)
#Check errors:
with self.assertRaises(MemoryError):
pygsti.do_mc2gst(ds,
gs_clgst,
self.lsgstStrings[0],
minProbClipForWeighting=1e-6,
probClipInterval=(-1e6, 1e6),
regularizeFactor=1e-3,
verbosity=0,
memLimit=1)
with self.assertRaises(NotImplementedError):
pygsti.do_mc2gst(
ds,
gs_clgst,
self.lsgstStrings[0],
minProbClipForWeighting=1e-6,
probClipInterval=(-1e6, 1e6),
regularizeFactor=1e-3,
verbosity=0,
cptp_penalty_factor=1.0) #cptp pentalty not implemented yet
# RUN BELOW LINES TO SEED SAVED GATESET FILES
#pygsti.io.write_gateset(gs_lsgst,compare_files + "/lsgst.gateset", "Saved LSGST Gateset")
#pygsti.io.write_gateset(gs_lsgst_reg,compare_files + "/lsgst_reg.gateset", "Saved LSGST Gateset w/Regularization")
gs_lsgst_compare = pygsti.io.load_gateset(compare_files +
"/lsgst.gateset")
gs_lsgst_reg_compare = pygsti.io.load_gateset(compare_files +
"/lsgst_reg.gateset")
gs_lsgst_go = pygsti.optimize_gauge(gs_lsgst,
'target',
targetGateset=gs_lsgst_compare,
spamWeight=1.0)
gs_lsgst_reg_go = pygsti.optimize_gauge(
gs_lsgst_reg,
'target',
targetGateset=gs_lsgst_reg_compare,
spamWeight=1.0)
self.assertAlmostEqual(gs_lsgst_go.frobeniusdist(gs_lsgst_compare),
0,
places=5)
self.assertAlmostEqual(
gs_lsgst_reg_go.frobeniusdist(gs_lsgst_reg_compare), 0, places=5)