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_MLGST(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")
mdl_clgst = mdl_clgst.depolarize(
op_noise=0.02, spam_noise=0.02
) # just to avoid infinity objective funct & jacs below
CM = profiler._get_mem_usage()
mdl_single_mlgst = pygsti.do_mlgst(ds,
mdl_clgst,
self.lsgstStrings[0],
minProbClip=1e-4,
probClipInterval=(-1e2, 1e2),
verbosity=0)
#this test often gives an assetion error "finite Jacobian has inf norm!" on Travis CI Python 3 case
try:
mdl_single_mlgst_cpsp = pygsti.do_mlgst(
ds,
mdl_clgst,
self.lsgstStrings[0],
minProbClip=1e-4,
probClipInterval=(-1e2, 1e2),
cptp_penalty_factor=1.0,
spam_penalty_factor=1.0,
verbosity=10) #uses both penalty factors w/verbosity > 0
except ValueError:
pass # ignore when assertions in customlm.py are disabled
except AssertionError:
pass # just ignore for now. FUTURE: see what we can do in custom LM about scaling large jacobians...
try:
mdl_single_mlgst_cp = pygsti.do_mlgst(ds,
mdl_clgst,
self.lsgstStrings[0],
minProbClip=1e-4,
probClipInterval=(-1e2, 1e2),
cptp_penalty_factor=1.0,
verbosity=10)
except ValueError:
pass # ignore when assertions in customlm.py are disabled
except AssertionError:
pass # just ignore for now. FUTURE: see what we can do in custom LM about scaling large jacobians...
try:
mdl_single_mlgst_sp = pygsti.do_mlgst(ds,
mdl_clgst,
self.lsgstStrings[0],
minProbClip=1e-4,
probClipInterval=(-1e2, 1e2),
spam_penalty_factor=1.0,
verbosity=10)
except ValueError:
pass # ignore when assertions in customlm.py are disabled
except AssertionError:
pass # just ignore for now. FUTURE: see what we can do in custom LM about scaling large jacobians...
mdl_mlegst = pygsti.do_iterative_mlgst(ds,
mdl_clgst,
self.lsgstStrings,
verbosity=0,
minProbClip=1e-4,
probClipInterval=(-1e2, 1e2),
memLimit=CM + 1024**3)
maxLogL, all_gs_mlegst_tups = pygsti.do_iterative_mlgst(
ds,
mdl_clgst,
[[mdl.tup for mdl in gsList] for gsList in self.lsgstStrings],
minProbClip=1e-4,
probClipInterval=(-1e2, 1e2),
returnAll=True,
returnMaxLogL=True)
mdl_mlegst_verb = self.runSilent(pygsti.do_iterative_mlgst,
ds,
mdl_clgst,
self.lsgstStrings,
verbosity=10,
minProbClip=1e-4,
probClipInterval=(-1e2, 1e2),
memLimit=CM + 1024**3)
self.assertAlmostEqual(mdl_mlegst.frobeniusdist(mdl_mlegst_verb),
0,
places=5)
self.assertAlmostEqual(mdl_mlegst.frobeniusdist(
all_gs_mlegst_tups[-1]),
0,
places=5)
#Run internal checks on less max-L values (so it doesn't take forever)
mdl_mlegst_chk = pygsti.do_iterative_mlgst(ds,
mdl_clgst,
self.lsgstStrings[0:2],
verbosity=0,
minProbClip=1e-4,
probClipInterval=(-1e2,
1e2),
check=True)
#Forcing function used by linear response error bars
forcingfn_grad = np.ones((1, mdl_clgst.num_params()), 'd')
mdl_lsgst_chk_opts3 = pygsti.algorithms.core._do_mlgst_base(
ds,
mdl_clgst,
self.lsgstStrings[0],
verbosity=0,
minProbClip=1e-4,
probClipInterval=(-1e2, 1e2),
forcefn_grad=forcingfn_grad)
with self.assertRaises(NotImplementedError):
# Non-poisson picture needs support for a non-leastsq solver (not impl yet)
mdl_lsgst_chk_opts4 = pygsti.algorithms.core._do_mlgst_base(
ds,
mdl_clgst,
self.lsgstStrings[0],
verbosity=0,
poissonPicture=False,
minProbClip=1e-4,
probClipInterval=(-1e2, 1e2),
forcefn_grad=forcingfn_grad) # non-poisson picture
#Check with small but ok memlimit -- not anymore since new mem estimation uses current memory, making this non-robust
#self.runSilent(pygsti.do_mlgst, ds, mdl_clgst, self.lsgstStrings[0], minProbClip=1e-6,
# probClipInterval=(-1e2,1e2), verbosity=4, memLimit=curMem+8500000) #invoke memory control
#non-Poisson picture - should use (-1,-1) model for consistency?
with self.assertRaises(NotImplementedError):
# Non-poisson picture needs support for a non-leastsq solver (not impl yet)
pygsti.do_mlgst(ds,
mdl_clgst,
self.lsgstStrings[0],
minProbClip=1e-4,
probClipInterval=(-1e2, 1e2),
verbosity=0,
poissonPicture=False)
try:
pygsti.do_mlgst(
ds,
mdl_clgst,
self.lsgstStrings[0],
minProbClip=1e-1, # 1e-1 b/c get inf Jacobians...
probClipInterval=(-1e2, 1e2),
verbosity=0,
poissonPicture=False,
spam_penalty_factor=1.0,
cptp_penalty_factor=1.0)
except ValueError:
pass # ignore when assertions in customlm.py are disabled
except AssertionError:
pass # just ignore for now. FUTURE: see what we can do in custom LM about scaling large jacobians...
#Check errors:
with self.assertRaises(MemoryError):
pygsti.do_mlgst(ds,
mdl_clgst,
self.lsgstStrings[0],
minProbClip=1e-4,
probClipInterval=(-1e2, 1e2),
verbosity=0,
memLimit=1)
# RUN BELOW LINES TO SEED SAVED GATESET FILES
if regenerate_references():
pygsti.io.write_model(mdl_mlegst, compare_files + "/mle_gst.model",
"Saved MLE-GST Model")
mdl_mle_compare = pygsti.io.load_model(compare_files +
"/mle_gst.model")
mdl_mlegst_go = pygsti.gaugeopt_to_target(mdl_mlegst,
mdl_mle_compare,
{'spam': 1.0},
checkJac=True)
self.assertAlmostEqual(mdl_mlegst_go.frobeniusdist(mdl_mle_compare),
0,
places=4)
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_logL_TP(self):
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)
#db = lsgst_gatesets_TP[-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
#Run a few tests to generate tables & figures we don't use in reports
self.results_logL.tables["chi2ProgressTable"]
self.results_logL.tables["logLProgressTable"]
self.results_logL.figures["bestEstimateSummedColorBoxPlot"]
self.results_logL.figures["blankBoxPlot"]
self.results_logL.figures["blankSummedBoxPlot"]
self.results_logL.figures["directLGSTColorBoxPlot"]
self.results_logL.figures["directLGSTDeviationColorBoxPlot"]
with self.assertRaises(KeyError):
self.results_logL.figures["FooBar"]
with self.assertRaises(KeyError):
self.results_logL._specials['FooBar']
#Run tests to generate tables we don't use in reports
self.results_logL.tables["bestGatesetVsTargetAnglesTable"]
self.results_logL.create_full_report_pdf(filename="temp_test_files/full_reportC.pdf", confidenceLevel=None,
debugAidsAppendix=False, gaugeOptAppendix=False,
pixelPlotAppendix=False, whackamoleAppendix=False,
verbosity=2)
self.results_logL.create_brief_report_pdf(filename="temp_test_files/brief_reportC.pdf", confidenceLevel=None, verbosity=2)
self.results_logL.create_presentation_pdf(filename="temp_test_files/slidesC.pdf", confidenceLevel=None,
debugAidsAppendix=False, pixelPlotAppendix=False, whackamoleAppendix=False,
verbosity=2)
if self.have_python_pptx:
self.results_logL.create_presentation_ppt(filename="temp_test_files/slidesC.ppt", confidenceLevel=None,
debugAidsAppendix=False, pixelPlotAppendix=False, whackamoleAppendix=False,
verbosity=2)
##Compare the text files, assume if these match the PDFs are equivalent
self.checkFile("full_reportC.tex")
self.checkFile("brief_reportC.tex")
self.checkFile("slidesC.tex")
self.results_logL.create_full_report_pdf(filename="temp_test_files/full_reportD.pdf", confidenceLevel=95,
debugAidsAppendix=True, gaugeOptAppendix=True,
pixelPlotAppendix=True, whackamoleAppendix=True,
verbosity=2)
self.results_logL.create_brief_report_pdf(filename="temp_test_files/brief_reportD.pdf", confidenceLevel=95, verbosity=2)
self.results_logL.create_presentation_pdf(filename="temp_test_files/slidesD.pdf", confidenceLevel=95,
debugAidsAppendix=True, pixelPlotAppendix=True, whackamoleAppendix=True,
verbosity=2)
if self.have_python_pptx:
self.results_logL.create_presentation_ppt(filename="temp_test_files/slidesD.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_reportD.tex")
self.checkFile("full_reportD_appendices.tex")
self.checkFile("brief_reportD.tex")
self.checkFile("slidesD.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_MLGST(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")
mdl_clgst = mdl_clgst.depolarize(op_noise=0.02, spam_noise=0.02) # just to avoid infinity objective funct & jacs below
CM = pygsti.baseobjs.profiler._get_mem_usage()
mdl_single_mlgst = pygsti.do_mlgst(ds, mdl_clgst, self.lsgstStrings[0], minProbClip=1e-4,
probClipInterval=(-1e2,1e2), verbosity=0)
#this test often gives an assetion error "finite Jacobian has inf norm!" on Travis CI Python 3 case
try:
mdl_single_mlgst_cpsp = pygsti.do_mlgst(ds, mdl_clgst, self.lsgstStrings[0], minProbClip=1e-4,
probClipInterval=(-1e2,1e2), cptp_penalty_factor=1.0,
spam_penalty_factor=1.0, verbosity=10) #uses both penalty factors w/verbosity > 0
except ValueError: pass # ignore when assertions in customlm.py are disabled
except AssertionError:
pass # just ignore for now. FUTURE: see what we can do in custom LM about scaling large jacobians...
try:
mdl_single_mlgst_cp = pygsti.do_mlgst(ds, mdl_clgst, self.lsgstStrings[0], minProbClip=1e-4,
probClipInterval=(-1e2,1e2), cptp_penalty_factor=1.0,
verbosity=10)
except ValueError: pass # ignore when assertions in customlm.py are disabled
except AssertionError:
pass # just ignore for now. FUTURE: see what we can do in custom LM about scaling large jacobians...
try:
mdl_single_mlgst_sp = pygsti.do_mlgst(ds, mdl_clgst, self.lsgstStrings[0], minProbClip=1e-4,
probClipInterval=(-1e2,1e2), spam_penalty_factor=1.0,
verbosity=10)
except ValueError: pass # ignore when assertions in customlm.py are disabled
except AssertionError:
pass # just ignore for now. FUTURE: see what we can do in custom LM about scaling large jacobians...
mdl_mlegst = pygsti.do_iterative_mlgst(ds, mdl_clgst, self.lsgstStrings, verbosity=0,
minProbClip=1e-4, probClipInterval=(-1e2,1e2),
memLimit=CM + 1024**3)
maxLogL, all_gs_mlegst_tups = pygsti.do_iterative_mlgst(
ds, mdl_clgst, [ [mdl.tup for mdl in gsList] for gsList in self.lsgstStrings],
minProbClip=1e-4, probClipInterval=(-1e2,1e2), returnAll=True, returnMaxLogL=True)
mdl_mlegst_verb = self.runSilent(pygsti.do_iterative_mlgst, ds, mdl_clgst, self.lsgstStrings, verbosity=10,
minProbClip=1e-4, probClipInterval=(-1e2,1e2),
memLimit=CM + 1024**3)
self.assertAlmostEqual(mdl_mlegst.frobeniusdist(mdl_mlegst_verb),0, places=5)
self.assertAlmostEqual(mdl_mlegst.frobeniusdist(all_gs_mlegst_tups[-1]),0,places=5)
#Run internal checks on less max-L values (so it doesn't take forever)
mdl_mlegst_chk = pygsti.do_iterative_mlgst(ds, mdl_clgst, self.lsgstStrings[0:2], verbosity=0,
minProbClip=1e-4, probClipInterval=(-1e2,1e2),
check=True)
#Other option variations - just make sure they run at this point
mdl_mlegst_chk_opts = pygsti.do_iterative_mlgst(ds, mdl_clgst, self.lsgstStrings[0:2], verbosity=0,
minProbClip=1e-4, probClipInterval=(-1e2,1e2),
circuitSetLabels=["Set1","Set2"], useFreqWeightedChiSq=True,
circuitWeightsDict={ (L('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_mlegst_chk_opts2 = pygsti.do_mlgst(ds, mdl_withA1, aliased_list, minProbClip=1e-4,
probClipInterval=(-1e2,1e2), verbosity=10,
opLabelAliases={ L('GA1'): (L('Gx'),) })
#Other option variations - just make sure they run at this point
mdl_mlegst_chk_opts3 = pygsti.do_iterative_mlgst(ds, mdl_clgst, self.lsgstStrings[0:2], verbosity=0,
minProbClip=1e-4, probClipInterval=(-1e2,1e2),
circuitSetLabels=["Set1","Set2"], useFreqWeightedChiSq=True,
circuitWeightsDict={ (L('Gx'),): 2.0 }, alwaysPerformMLE=True )
#Forcing function used by linear response error bars
forcingfn_grad = np.ones((1,mdl_clgst.num_params()), 'd')
mdl_lsgst_chk_opts3 = pygsti.algorithms.core._do_mlgst_base(
ds, mdl_clgst, self.lsgstStrings[0], verbosity=0,
minProbClip=1e-4, probClipInterval=(-1e2,1e2),
forcefn_grad=forcingfn_grad)
with self.assertRaises(NotImplementedError):
# Non-poisson picture needs support for a non-leastsq solver (not impl yet)
mdl_lsgst_chk_opts4 = pygsti.algorithms.core._do_mlgst_base(
ds, mdl_clgst, self.lsgstStrings[0], verbosity=0, poissonPicture=False,
minProbClip=1e-4, probClipInterval=(-1e2,1e2),
forcefn_grad=forcingfn_grad) # non-poisson picture
#Check with small but ok memlimit -- not anymore since new mem estimation uses current memory, making this non-robust
#self.runSilent(pygsti.do_mlgst, ds, mdl_clgst, self.lsgstStrings[0], minProbClip=1e-6,
# probClipInterval=(-1e2,1e2), verbosity=4, memLimit=curMem+8500000) #invoke memory control
#non-Poisson picture - should use (-1,-1) model for consistency?
with self.assertRaises(NotImplementedError):
# Non-poisson picture needs support for a non-leastsq solver (not impl yet)
pygsti.do_mlgst(ds, mdl_clgst, self.lsgstStrings[0], minProbClip=1e-4,
probClipInterval=(-1e2,1e2), verbosity=0, poissonPicture=False)
try:
pygsti.do_mlgst(ds, mdl_clgst, self.lsgstStrings[0], minProbClip=1e-1, # 1e-1 b/c get inf Jacobians...
probClipInterval=(-1e2,1e2), verbosity=0, poissonPicture=False,
spam_penalty_factor=1.0, cptp_penalty_factor=1.0)
except ValueError: pass # ignore when assertions in customlm.py are disabled
except AssertionError:
pass # just ignore for now. FUTURE: see what we can do in custom LM about scaling large jacobians...
#Check errors:
with self.assertRaises(MemoryError):
pygsti.do_mlgst(ds, mdl_clgst, self.lsgstStrings[0], minProbClip=1e-4,
probClipInterval=(-1e2,1e2),verbosity=0, memLimit=1)
# 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_mlegst,compare_files + "/mle_gst.model", "Saved MLE-GST Model")
mdl_mle_compare = pygsti.io.load_model(compare_files + "/mle_gst.model")
mdl_mlegst_go = pygsti.gaugeopt_to_target(mdl_mlegst, mdl_mle_compare, {'spam':1.0}, checkJac=True)
self.assertAlmostEqual( mdl_mlegst_go.frobeniusdist(mdl_mle_compare), 0, places=4)
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 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