def test_LGST(self):
ds = self.ds
print("GG0 = ", self.model.default_gauge_group)
mdl_lgst = pygsti.do_lgst(ds,
self.fiducials,
self.fiducials,
self.model,
svdTruncateTo=4,
verbosity=0)
mdl_lgst_verb = self.runSilent(pygsti.do_lgst,
ds,
self.fiducials,
self.fiducials,
self.model,
svdTruncateTo=4,
verbosity=10)
self.assertAlmostEqual(mdl_lgst.frobeniusdist(mdl_lgst_verb), 0)
print("GG = ", mdl_lgst.default_gauge_group)
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")
# RUN BELOW LINES TO SEED SAVED GATESET FILES
if regenerate_references():
pygsti.io.write_model(
mdl_lgst, compare_files + "/lgst.model",
"Saved LGST Model before gauge optimization")
pygsti.io.write_model(mdl_lgst_go,
compare_files + "/lgst_go.model",
"Saved LGST Model after gauge optimization")
pygsti.io.write_model(
mdl_clgst, compare_files + "/clgst.model",
"Saved LGST Model after G.O. and CPTP contraction")
mdl_lgst_compare = pygsti.io.load_model(compare_files + "/lgst.model")
mdl_lgst_go_compare = pygsti.io.load_model(compare_files +
"/lgst_go.model")
mdl_clgst_compare = pygsti.io.load_model(compare_files +
"/clgst.model")
self.assertAlmostEqual(mdl_lgst.frobeniusdist(mdl_lgst_compare),
0,
places=5)
self.assertAlmostEqual(mdl_lgst_go.frobeniusdist(mdl_lgst_go_compare),
0,
places=5)
self.assertAlmostEqual(mdl_clgst.frobeniusdist(mdl_clgst_compare),
0,
places=5)
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
def test_eLGST(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_exlgst = pygsti.do_exlgst(ds, gs_clgst, self.elgstStrings[0], self.specs,
self.gateset, regularizeFactor=1e-3, svdTruncateTo=4,
verbosity=0)
gs_single_exlgst_verb = self.runSilent(pygsti.do_exlgst, ds, gs_clgst, self.elgstStrings[0], self.specs,
self.gateset, regularizeFactor=1e-3, svdTruncateTo=4,
verbosity=10)
gs_exlgst = pygsti.do_iterative_exlgst(ds, gs_clgst, self.specs, self.elgstStrings,
targetGateset=self.gateset, svdTruncateTo=4, verbosity=0)
all_minErrs, all_gs_exlgst_tups = pygsti.do_iterative_exlgst(
ds, gs_clgst, self.specs, [ [gs.tup for gs in gsList] for gsList in self.elgstStrings],
targetGateset=self.gateset, svdTruncateTo=4, verbosity=0, returnAll=True, returnErrorVec=True)
gs_exlgst_verb = self.runSilent(pygsti.do_iterative_exlgst, ds, gs_clgst, self.specs, self.elgstStrings,
targetGateset=self.gateset, svdTruncateTo=4, verbosity=10)
gs_exlgst_reg = pygsti.do_iterative_exlgst(ds, gs_clgst, self.specs, self.elgstStrings,
targetGateset=self.gateset, svdTruncateTo=4, verbosity=0,
regularizeFactor=10)
self.assertAlmostEqual(gs_exlgst.frobeniusdist(gs_exlgst_verb),0)
self.assertAlmostEqual(gs_exlgst.frobeniusdist(all_gs_exlgst_tups[-1]),0)
#Run internal checks on less max-L values (so it doesn't take forever)
gs_exlgst_chk = pygsti.do_iterative_exlgst(ds, gs_clgst, self.specs, self.elgstStrings[0:2],
targetGateset=self.gateset, svdTruncateTo=4, verbosity=0,
check_jacobian=True)
gs_exlgst_chk_verb = self.runSilent(pygsti.do_iterative_exlgst,ds, gs_clgst, self.specs, self.elgstStrings[0:2],
targetGateset=self.gateset, svdTruncateTo=4, verbosity=10,
check_jacobian=True)
# RUN BELOW LINES TO SEED SAVED GATESET FILES
#pygsti.io.write_gateset(gs_exlgst,compare_files + "/exlgst.gateset", "Saved Extended-LGST (eLGST) Gateset")
#pygsti.io.write_gateset(gs_exlgst_reg,compare_files + "/exlgst_reg.gateset", "Saved Extended-LGST (eLGST) Gateset w/regularization")
gs_exlgst_compare = pygsti.io.load_gateset(compare_files + "/exlgst.gateset")
gs_exlgst_reg_compare = pygsti.io.load_gateset(compare_files + "/exlgst_reg.gateset")
gs_exlgst_go = pygsti.optimize_gauge(gs_exlgst, 'target', targetGateset=gs_exlgst_compare, spamWeight=1.0)
gs_exlgst_reg_go = pygsti.optimize_gauge(gs_exlgst_reg, 'target', targetGateset=gs_exlgst_reg_compare, spamWeight=1.0)
self.assertAlmostEqual( gs_exlgst_go.frobeniusdist(gs_exlgst_compare), 0, places=5)
self.assertAlmostEqual( gs_exlgst_reg_go.frobeniusdist(gs_exlgst_reg_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_LGST(self):
ds = self.ds
#pygsti.construction.generate_fake_data(self.datagen_gateset, self.lgstStrings, nSamples=1000,
# sampleError='binomial', seed=None)
gs_lgst = pygsti.do_lgst(ds, self.specs, self.gateset, svdTruncateTo=4, verbosity=0)
gs_lgst_verb = self.runSilent(pygsti.do_lgst, ds, self.specs, self.gateset, svdTruncateTo=4, verbosity=10)
self.assertAlmostEqual(gs_lgst.frobeniusdist(gs_lgst_verb),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")
# RUN BELOW LINES TO SEED SAVED GATESET FILES
#pygsti.io.write_gateset(gs_lgst,compare_files + "/lgst.gateset", "Saved LGST Gateset before gauge optimization")
#pygsti.io.write_gateset(gs_lgst_go,compare_files + "/lgst_go.gateset", "Saved LGST Gateset after gauge optimization")
#pygsti.io.write_gateset(gs_clgst,compare_files + "/clgst.gateset", "Saved LGST Gateset after G.O. and CPTP contraction")
gs_lgst_compare = pygsti.io.load_gateset(compare_files + "/lgst.gateset")
gs_lgst_go_compare = pygsti.io.load_gateset(compare_files + "/lgst_go.gateset")
gs_clgst_compare = pygsti.io.load_gateset(compare_files + "/clgst.gateset")
self.assertAlmostEqual( gs_lgst.frobeniusdist(gs_lgst_compare), 0)
self.assertAlmostEqual( gs_lgst_go.frobeniusdist(gs_lgst_go_compare), 0)
self.assertAlmostEqual( gs_clgst.frobeniusdist(gs_clgst_compare), 0)
#Check for error conditions
with self.assertRaises(ValueError):
gs_lgst = pygsti.do_lgst(ds, self.specs, None, svdTruncateTo=4, verbosity=0) #no gate labels
with self.assertRaises(ValueError):
gs_lgst = pygsti.do_lgst(ds, self.specs, None, gateLabels=list(self.gateset.gates.keys()),
svdTruncateTo=4, verbosity=0) #no spam dict
with self.assertRaises(ValueError):
gs_lgst = pygsti.do_lgst(ds, self.specs, None, gateLabels=list(self.gateset.gates.keys()),
spamDict=self.gateset.get_reverse_spam_defs(),
svdTruncateTo=4, verbosity=0) #no identity vector
with self.assertRaises(ValueError):
bad_specs = pygsti.construction.build_spam_specs(
pygsti.construction.gatestring_list([('Gx',),('Gx',),('Gx',),('Gx',)]), effect_labels=['E0'])
gs_lgst = pygsti.do_lgst(ds, bad_specs, self.gateset, svdTruncateTo=4, verbosity=0) # bad specs (rank deficient)
with self.assertRaises(KeyError): # AB-matrix construction error
incomplete_strings = self.lgstStrings[5:] #drop first 5 strings...
bad_ds = pygsti.construction.generate_fake_data(
self.datagen_gateset, incomplete_strings,
nSamples=10, sampleError='none')
gs_lgst = pygsti.do_lgst(bad_ds, self.specs, self.gateset,
svdTruncateTo=4, verbosity=0)
# incomplete dataset
with self.assertRaises(KeyError): # X-matrix construction error
incomplete_strings = self.lgstStrings[:-5] #drop last 5 strings...
bad_ds = pygsti.construction.generate_fake_data(
self.datagen_gateset, incomplete_strings,
nSamples=10, sampleError='none')
gs_lgst = pygsti.do_lgst(bad_ds, self.specs, self.gateset,
svdTruncateTo=4, verbosity=0)
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_LGST(self):
ds = self.ds
#pygsti.construction.generate_fake_data(self.datagen_gateset, self.lgstStrings, nSamples=1000,
# sampleError='binomial', seed=None)
print("GG0 = ",self.gateset.default_gauge_group)
gs_lgst = pygsti.do_lgst(ds, self.fiducials, self.fiducials, self.gateset, svdTruncateTo=4, verbosity=0)
gs_lgst_verb = self.runSilent(pygsti.do_lgst, ds, self.fiducials, self.fiducials, self.gateset, svdTruncateTo=4, verbosity=10)
self.assertAlmostEqual(gs_lgst.frobeniusdist(gs_lgst_verb),0)
print("GG = ",gs_lgst.default_gauge_group)
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")
# RUN BELOW LINES TO SEED SAVED GATESET FILES
#pygsti.io.write_gateset(gs_lgst,compare_files + "/lgst.gateset", "Saved LGST Gateset before gauge optimization")
#pygsti.io.write_gateset(gs_lgst_go,compare_files + "/lgst_go.gateset", "Saved LGST Gateset after gauge optimization")
#pygsti.io.write_gateset(gs_clgst,compare_files + "/clgst.gateset", "Saved LGST Gateset after G.O. and CPTP contraction")
gs_lgst_compare = pygsti.io.load_gateset(compare_files + "/lgst.gateset")
gs_lgst_go_compare = pygsti.io.load_gateset(compare_files + "/lgst_go.gateset")
gs_clgst_compare = pygsti.io.load_gateset(compare_files + "/clgst.gateset")
self.assertAlmostEqual( gs_lgst.frobeniusdist(gs_lgst_compare), 0, places=5)
self.assertAlmostEqual( gs_lgst_go.frobeniusdist(gs_lgst_go_compare), 0, places=5)
self.assertAlmostEqual( gs_clgst.frobeniusdist(gs_clgst_compare), 0, places=5)
#Check for error conditions
with self.assertRaises(ValueError):
gs_lgst = pygsti.do_lgst(ds, self.fiducials, self.fiducials, None, svdTruncateTo=4, verbosity=0) #no target gateset
with self.assertRaises(ValueError):
gs_lgst = pygsti.do_lgst(ds, self.fiducials, self.fiducials, None, gateLabels=list(self.gateset.gates.keys()),
svdTruncateTo=4, verbosity=0) #no spam dict
#No need for identity vector anymore
#with self.assertRaises(ValueError):
# gs_lgst = pygsti.do_lgst(ds, self.fiducials, self.fiducials, None, gateLabels=list(self.gateset.gates.keys()),
# spamDict=self.gateset.get_reverse_spam_defs(),
# svdTruncateTo=4, verbosity=0) #no identity vector
with self.assertRaises(ValueError):
bad_fids =pygsti.construction.gatestring_list([('Gx',),('Gx',),('Gx',),('Gx',)])
gs_lgst = pygsti.do_lgst(ds, bad_fids, bad_fids, self.gateset, svdTruncateTo=4, verbosity=0) # bad fiducials (rank deficient)
with self.assertRaises(KeyError): # AB-matrix construction error
incomplete_strings = self.lgstStrings[5:] #drop first 5 strings...
bad_ds = pygsti.construction.generate_fake_data(
self.datagen_gateset, incomplete_strings,
nSamples=10, sampleError='none')
gs_lgst = pygsti.do_lgst(bad_ds, self.fiducials, self.fiducials, self.gateset,
svdTruncateTo=4, verbosity=0)
# incomplete dataset
with self.assertRaises(KeyError): # X-matrix construction error
incomplete_strings = self.lgstStrings[:-5] #drop last 5 strings...
bad_ds = pygsti.construction.generate_fake_data(
self.datagen_gateset, incomplete_strings,
nSamples=10, sampleError='none')
gs_lgst = pygsti.do_lgst(bad_ds, self.fiducials, self.fiducials, self.gateset,
svdTruncateTo=4, verbosity=0)
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 test_eLGST(self):
ds = self.ds
#pygsti.construction.generate_fake_data(self.datagen_gateset, self.lsgstStrings[-1],
# nSamples=1000,sampleError='binomial', seed=100)
assert(pygsti.obj.GateSet._pcheck)
gs_lgst = pygsti.do_lgst(ds, self.fiducials, self.fiducials, self.gateset, svdTruncateTo=4, verbosity=0)
gs_lgst._check_paramvec()
gs_lgst_go = pygsti.gaugeopt_to_target(gs_lgst,self.gateset, {'spam':1.0, 'gates': 1.0}, checkJac=True)
gs_lgst_go._check_paramvec()
gs_clgst = pygsti.contract(gs_lgst_go, "CPTP")
gs_clgst._check_paramvec()
self.gateset._check_paramvec()
_,gs_single_exlgst = pygsti.do_exlgst(ds, gs_clgst, self.elgstStrings[0], self.fiducials, self.fiducials,
self.gateset, regularizeFactor=1e-3, svdTruncateTo=4,
verbosity=0)
gs_single_exlgst._check_paramvec()
_,gs_single_exlgst_verb = self.runSilent(pygsti.do_exlgst, ds, gs_clgst, self.elgstStrings[0], self.fiducials, self.fiducials,
self.gateset, regularizeFactor=1e-3, svdTruncateTo=4,
verbosity=10)
gs_single_exlgst_verb._check_paramvec()
self.assertAlmostEqual(gs_single_exlgst.frobeniusdist(gs_single_exlgst_verb),0)
gs_exlgst = pygsti.do_iterative_exlgst(ds, gs_clgst, self.fiducials, self.fiducials, self.elgstStrings,
targetGateset=self.gateset, svdTruncateTo=4, verbosity=0)
all_minErrs, all_gs_exlgst_tups = pygsti.do_iterative_exlgst(
ds, gs_clgst, self.fiducials, self.fiducials, [ [gs.tup for gs in gsList] for gsList in self.elgstStrings],
targetGateset=self.gateset, svdTruncateTo=4, verbosity=0, returnAll=True, returnErrorVec=True)
gs_exlgst_verb = self.runSilent(pygsti.do_iterative_exlgst, ds, gs_clgst, self.fiducials, self.fiducials, self.elgstStrings,
targetGateset=self.gateset, svdTruncateTo=4, verbosity=10)
gs_exlgst_reg = pygsti.do_iterative_exlgst(ds, gs_clgst, self.fiducials, self.fiducials, self.elgstStrings,
targetGateset=self.gateset, svdTruncateTo=4, verbosity=0,
regularizeFactor=10)
self.assertAlmostEqual(gs_exlgst.frobeniusdist(gs_exlgst_verb),0)
self.assertAlmostEqual(gs_exlgst.frobeniusdist(all_gs_exlgst_tups[-1]),0)
#Run internal checks on less max-L values (so it doesn't take forever)
gs_exlgst_chk = pygsti.do_iterative_exlgst(ds, gs_clgst, self.fiducials, self.fiducials, self.elgstStrings[0:2],
targetGateset=self.gateset, svdTruncateTo=4, verbosity=0,
check_jacobian=True)
gs_exlgst_chk_verb = self.runSilent(pygsti.do_iterative_exlgst,ds, gs_clgst, self.fiducials, self.fiducials, self.elgstStrings[0:2],
targetGateset=self.gateset, svdTruncateTo=4, verbosity=10,
check_jacobian=True)
# RUN BELOW LINES TO SEED SAVED GATESET FILES
#pygsti.io.write_gateset(gs_exlgst,compare_files + "/exlgst.gateset", "Saved Extended-LGST (eLGST) Gateset")
#pygsti.io.write_gateset(gs_exlgst_reg,compare_files + "/exlgst_reg.gateset", "Saved Extended-LGST (eLGST) Gateset w/regularization")
gs_exlgst_compare = pygsti.io.load_gateset(compare_files + "/exlgst.gateset")
gs_exlgst_reg_compare = pygsti.io.load_gateset(compare_files + "/exlgst_reg.gateset")
gs_exlgst.set_all_parameterizations("full") # b/c ex-LGST sets spam to StaticSPAMVec objects (b/c they're not optimized)
gs_exlgst_reg.set_all_parameterizations("full") # b/c ex-LGST sets spam to StaticSPAMVec objects (b/c they're not optimized)
gs_exlgst_go = pygsti.gaugeopt_to_target(gs_exlgst,gs_exlgst_compare, {'spam':1.0 }, checkJac=True)
gs_exlgst_reg_go = pygsti.gaugeopt_to_target(gs_exlgst_reg,gs_exlgst_reg_compare, {'spam':1.0 }, checkJac=True)
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_gaugeopt_and_contract(self):
ds = self.ds_lgst
#pygsti.construction.generate_fake_data(self.datagen_gateset, self.lgstStrings,
# nSamples=10000,sampleError='binomial', seed=100)
gs_lgst = pygsti.do_lgst(ds,
self.specs,
self.gateset,
svdTruncateTo=4,
verbosity=0)
#Gauge Opt to Target
gs_lgst_target = self.runSilent(pygsti.optimize_gauge,
gs_lgst,
"target",
targetGateset=self.gateset,
verbosity=10)
#Gauge Opt to Target using non-frobenius metrics
gs_lgst_targetAlt = self.runSilent(pygsti.optimize_gauge,
gs_lgst_target,
"target",
targetGateset=self.gateset,
targetGatesMetric='fidelity',
verbosity=10)
gs_lgst_targetAlt = self.runSilent(pygsti.optimize_gauge,
gs_lgst_target,
"target",
targetGateset=self.gateset,
targetGatesMetric='tracedist',
verbosity=10)
gs_lgst_targetAlt = self.runSilent(pygsti.optimize_gauge,
gs_lgst_target,
"target",
targetGateset=self.gateset,
targetSpamMetric='fidelity',
verbosity=10)
gs_lgst_targetAlt = self.runSilent(pygsti.optimize_gauge,
gs_lgst_target,
"target",
targetGateset=self.gateset,
targetSpamMetric='tracedist',
verbosity=10)
with self.assertRaises(ValueError):
self.runSilent(pygsti.optimize_gauge,
gs_lgst_target,
"target",
targetGateset=self.gateset,
targetGatesMetric='foobar',
verbosity=10) #bad targetGatesMetric
with self.assertRaises(ValueError):
self.runSilent(pygsti.optimize_gauge,
gs_lgst_target,
"target",
targetGateset=self.gateset,
targetSpamMetric='foobar',
verbosity=10) #bad targetSpamMetric
with self.assertRaises(ValueError):
self.runSilent(pygsti.optimize_gauge,
gs_lgst_target,
"foobar",
targetGateset=self.gateset,
targetSpamMetric='target',
verbosity=10) #bad toGetTo
#Contractions
gs_clgst_tp = self.runSilent(pygsti.contract,
gs_lgst_target,
"TP",
verbosity=10,
tol=10.0)
gs_clgst_cp = self.runSilent(pygsti.contract,
gs_lgst_target,
"CP",
verbosity=10,
tol=10.0)
gs_clgst_cptp = self.runSilent(pygsti.contract,
gs_lgst_target,
"CPTP",
verbosity=10,
tol=10.0)
gs_clgst_cptp2 = self.runSilent(pygsti.contract,
gs_lgst_target,
"CPTP",
verbosity=10,
useDirectCP=False)
gs_clgst_cptp3 = self.runSilent(pygsti.contract,
gs_lgst_target,
"CPTP",
verbosity=10,
tol=10.0,
maxiter=0)
gs_clgst_xp = self.runSilent(pygsti.contract,
gs_lgst_target,
"XP",
ds,
verbosity=10,
tol=10.0)
gs_clgst_xptp = self.runSilent(pygsti.contract,
gs_lgst_target,
"XPTP",
ds,
verbosity=10,
tol=10.0)
gs_clgst_vsp = self.runSilent(pygsti.contract,
gs_lgst_target,
"vSPAM",
verbosity=10,
tol=10.0)
gs_clgst_none = self.runSilent(pygsti.contract,
gs_lgst_target,
"nothing",
verbosity=10,
tol=10.0)
#test bad effect vector cases
gs_bad_effect = gs_lgst_target.copy()
gs_bad_effect.effects['E0'] = [100.0, 0, 0, 0] # E eigvals all > 1.0
self.runSilent(pygsti.contract,
gs_bad_effect,
"vSPAM",
verbosity=10,
tol=10.0)
gs_bad_effect.effects['E0'] = [-100.0, 0, 0, 0] # E eigvals all < 0
self.runSilent(pygsti.contract,
gs_bad_effect,
"vSPAM",
verbosity=10,
tol=10.0)
with self.assertRaises(ValueError):
self.runSilent(pygsti.contract,
gs_lgst_target,
"foobar",
verbosity=10,
tol=10.0) #bad toWhat
#More gauge optimizations
gs_lgst_target_cp = self.runSilent(pygsti.optimize_gauge,
gs_clgst_cptp,
"target",
targetGateset=self.gateset,
constrainToCP=True,
constrainToTP=True,
constrainToValidSpam=True,
verbosity=10)
gs_lgst_cptp = self.runSilent(pygsti.optimize_gauge,
gs_lgst,
"CPTP",
verbosity=10)
gs_lgst_cptp_tp = self.runSilent(pygsti.optimize_gauge,
gs_lgst,
"CPTP",
verbosity=10,
constrainToTP=True)
gs_lgst_tp = self.runSilent(pygsti.optimize_gauge,
gs_lgst,
"TP",
verbosity=10)
gs_lgst_tptarget = self.runSilent(pygsti.optimize_gauge,
gs_lgst,
"TP and target",
targetGateset=self.gateset,
verbosity=10)
gs_lgst_cptptarget = self.runSilent(pygsti.optimize_gauge,
gs_lgst,
"CPTP and target",
targetGateset=self.gateset,
verbosity=10)
gs_lgst_cptptarget2 = self.runSilent(pygsti.optimize_gauge,
gs_lgst,
"CPTP and target",
targetGateset=self.gateset,
verbosity=10,
constrainToTP=True)
gs_lgst_cd = self.runSilent(pygsti.optimize_gauge,
gs_lgst,
"Completely Depolarized",
targetGateset=self.gateset,
verbosity=10)
#TODO: check output lies in space desired
# big kick that should land it outside XP, TP, etc, so contraction
# routines are more tested
gs_bigkick = gs_lgst_target.kick(absmag=1.0)
gs_badspam = gs_bigkick.copy()
gs_badspam.effects['E0'] = np.array(
[[2], [0], [0], [4]], 'd') #set a bad evec so vSPAM has to work...
gs_clgst_tp = self.runSilent(pygsti.contract,
gs_bigkick,
"TP",
verbosity=10,
tol=10.0)
gs_clgst_cp = self.runSilent(pygsti.contract,
gs_bigkick,
"CP",
verbosity=10,
tol=10.0)
gs_clgst_cptp = self.runSilent(pygsti.contract,
gs_bigkick,
"CPTP",
verbosity=10,
tol=10.0)
gs_clgst_xp = self.runSilent(pygsti.contract,
gs_bigkick,
"XP",
ds,
verbosity=10,
tol=10.0)
gs_clgst_xptp = self.runSilent(pygsti.contract,
gs_bigkick,
"XPTP",
ds,
verbosity=10,
tol=10.0)
gs_clgst_vsp = self.runSilent(pygsti.contract,
gs_badspam,
"vSPAM",
verbosity=10,
tol=10.0)
gs_clgst_none = self.runSilent(pygsti.contract,
gs_bigkick,
"nothing",
verbosity=10,
tol=10.0)
#TODO: check output lies in space desired
#Check Errors
with self.assertRaises(ValueError):
pygsti.optimize_gauge(gs_lgst, "FooBar",
verbosity=0) # bad toGetTo argument
with self.assertRaises(ValueError):
pygsti.contract(gs_lgst_target, "FooBar",
verbosity=0) # bad toWhat argument
def test_LGST(self):
ds = self.ds
#pygsti.construction.generate_fake_data(self.datagen_gateset, self.lgstStrings, nSamples=1000,
# sampleError='binomial', seed=None)
gs_lgst = pygsti.do_lgst(ds,
self.specs,
self.gateset,
svdTruncateTo=4,
verbosity=0)
gs_lgst_verb = self.runSilent(pygsti.do_lgst,
ds,
self.specs,
self.gateset,
svdTruncateTo=4,
verbosity=10)
self.assertAlmostEqual(gs_lgst.frobeniusdist(gs_lgst_verb), 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")
# RUN BELOW LINES TO SEED SAVED GATESET FILES
#pygsti.io.write_gateset(gs_lgst,compare_files + "/lgst.gateset", "Saved LGST Gateset before gauge optimization")
#pygsti.io.write_gateset(gs_lgst_go,compare_files + "/lgst_go.gateset", "Saved LGST Gateset after gauge optimization")
#pygsti.io.write_gateset(gs_clgst,compare_files + "/clgst.gateset", "Saved LGST Gateset after G.O. and CPTP contraction")
gs_lgst_compare = pygsti.io.load_gateset(compare_files +
"/lgst.gateset")
gs_lgst_go_compare = pygsti.io.load_gateset(compare_files +
"/lgst_go.gateset")
gs_clgst_compare = pygsti.io.load_gateset(compare_files +
"/clgst.gateset")
self.assertAlmostEqual(gs_lgst.frobeniusdist(gs_lgst_compare), 0)
self.assertAlmostEqual(gs_lgst_go.frobeniusdist(gs_lgst_go_compare), 0)
self.assertAlmostEqual(gs_clgst.frobeniusdist(gs_clgst_compare), 0)
#Check for error conditions
with self.assertRaises(ValueError):
gs_lgst = pygsti.do_lgst(ds,
self.specs,
None,
svdTruncateTo=4,
verbosity=0) #no gate labels
with self.assertRaises(ValueError):
gs_lgst = pygsti.do_lgst(ds,
self.specs,
None,
gateLabels=list(
self.gateset.gates.keys()),
svdTruncateTo=4,
verbosity=0) #no spam dict
with self.assertRaises(ValueError):
gs_lgst = pygsti.do_lgst(
ds,
self.specs,
None,
gateLabels=list(self.gateset.gates.keys()),
spamDict=self.gateset.get_reverse_spam_defs(),
svdTruncateTo=4,
verbosity=0) #no identity vector
with self.assertRaises(ValueError):
bad_specs = pygsti.construction.build_spam_specs(
pygsti.construction.gatestring_list([('Gx', ), ('Gx', ),
('Gx', ), ('Gx', )]),
effect_labels=['E0'])
gs_lgst = pygsti.do_lgst(ds,
bad_specs,
self.gateset,
svdTruncateTo=4,
verbosity=0) # bad specs (rank deficient)
with self.assertRaises(KeyError): # AB-matrix construction error
incomplete_strings = self.lgstStrings[5:] #drop first 5 strings...
bad_ds = pygsti.construction.generate_fake_data(
self.datagen_gateset,
incomplete_strings,
nSamples=10,
sampleError='none')
gs_lgst = pygsti.do_lgst(bad_ds,
self.specs,
self.gateset,
svdTruncateTo=4,
verbosity=0)
# incomplete dataset
with self.assertRaises(KeyError): # X-matrix construction error
incomplete_strings = self.lgstStrings[:-5] #drop last 5 strings...
bad_ds = pygsti.construction.generate_fake_data(
self.datagen_gateset,
incomplete_strings,
nSamples=10,
sampleError='none')
gs_lgst = pygsti.do_lgst(bad_ds,
self.specs,
self.gateset,
svdTruncateTo=4,
verbosity=0)
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_gaugeopt_and_contract(self):
ds = self.ds_lgst
#pygsti.construction.generate_fake_data(self.datagen_gateset, self.lgstStrings,
# nSamples=10000,sampleError='binomial', seed=100)
mdl_lgst = pygsti.do_lgst(ds, self.fiducials, self.fiducials, self.model, svdTruncateTo=4, verbosity=0)
#Gauge Opt to Target
mdl_lgst_target = self.runSilent(pygsti.gaugeopt_to_target, mdl_lgst, self.model, verbosity=10, checkJac=True)
#
mdl_lgst.basis = self.model.basis.copy()
mdl_clgst_cp = self.runSilent(pygsti.contract, mdl_lgst, "CP",verbosity=10, tol=10.0, useDirectCP=False) #non-direct CP contraction
#Gauge Opt to Target using non-frobenius metrics
mdl_lgst_targetAlt = self.runSilent(pygsti.gaugeopt_to_target, mdl_lgst_target, self.model,
gatesMetric='fidelity', verbosity=10, checkJac=True)
mdl_lgst_targetAlt = self.runSilent(pygsti.gaugeopt_to_target, mdl_lgst_target, self.model,
gatesMetric='tracedist', verbosity=10, checkJac=True)
mdl_lgst_targetAlt = self.runSilent(pygsti.gaugeopt_to_target, mdl_lgst_target, self.model,
spamMetric='fidelity', verbosity=10, checkJac=True)
mdl_lgst_targetAlt = self.runSilent(pygsti.gaugeopt_to_target, mdl_lgst_target, self.model,
spamMetric='tracedist', verbosity=10, checkJac=True)
#Using other methods
mdl_BFGS = self.runSilent(pygsti.gaugeopt_to_target, mdl_lgst, self.model, method='BFGS', verbosity=10)
with self.assertRaises(ValueError): #Invalid metric
self.runSilent(pygsti.gaugeopt_to_target, mdl_lgst, self.model, method='BFGS', spamMetric='foobar', verbosity=10)
with self.assertRaises(ValueError): #Invalid metric
self.runSilent(pygsti.gaugeopt_to_target, mdl_lgst, self.model, method='BFGS', gatesMetric='foobar', verbosity=10)
with self.assertRaises(ValueError): #can't use least-squares for anything but frobenius metric
self.runSilent(pygsti.gaugeopt_to_target, mdl_lgst_target, self.model,
spamMetric='tracedist', method='ls', verbosity=10, checkJac=True)
#with self.assertRaises(ValueError):
# self.runSilent(pygsti.gaugeopt_to_target, mdl_lgst_target, self.model,
# gatesMetric='foobar', verbosity=10) #bad gatesMetric
#
#with self.assertRaises(ValueError):
# self.runSilent(pygsti.gaugeopt_to_target, mdl_lgst_target, self.model,
# spamMetric='foobar', verbosity=10) #bad spamMetric
#Contractions
mdl_clgst_tp = self.runSilent(pygsti.contract, mdl_lgst_target, "TP",verbosity=10, tol=10.0)
mdl_clgst_cp = self.runSilent(pygsti.contract, mdl_lgst_target, "CP",verbosity=10, tol=10.0)
mdl_clgst_cp2 = self.runSilent(pygsti.contract, mdl_lgst_target, "CP",verbosity=10, tol=10.0)
mdl_clgst_cptp = self.runSilent(pygsti.contract, mdl_lgst_target, "CPTP",verbosity=10, tol=10.0)
mdl_clgst_cptp2 = self.runSilent(pygsti.contract, mdl_lgst_target, "CPTP",verbosity=10, useDirectCP=False)
mdl_clgst_cptp3 = self.runSilent(pygsti.contract, mdl_lgst_target, "CPTP",verbosity=10, tol=10.0, maxiter=0)
mdl_clgst_xp = self.runSilent(pygsti.contract, mdl_lgst_target, "XP", ds,verbosity=10, tol=10.0)
mdl_clgst_xptp = self.runSilent(pygsti.contract, mdl_lgst_target, "XPTP", ds,verbosity=10, tol=10.0)
mdl_clgst_vsp = self.runSilent(pygsti.contract, mdl_lgst_target, "vSPAM",verbosity=10, tol=10.0)
mdl_clgst_none = self.runSilent(pygsti.contract, mdl_lgst_target, "nothing",verbosity=10, tol=10.0)
#test bad effect vector cases
mdl_bad_effect = mdl_lgst_target.copy()
mdl_bad_effect.povms['Mdefault'] = pygsti.obj.UnconstrainedPOVM( [('0',[100.0,0,0,0])] ) # E eigvals all > 1.0
self.runSilent(pygsti.contract, mdl_bad_effect, "vSPAM",verbosity=10, tol=10.0)
mdl_bad_effect.povms['Mdefault'] = pygsti.obj.UnconstrainedPOVM( [('0',[-100.0,0,0,0])] ) # E eigvals all < 0
self.runSilent(pygsti.contract, mdl_bad_effect, "vSPAM",verbosity=10, tol=10.0)
#with self.assertRaises(ValueError):
# self.runSilent(pygsti.contract, mdl_lgst_target, "foobar",verbosity=10, tol=10.0) #bad toWhat
#More gauge optimizations
TP_gauge_group = pygsti.obj.TPGaugeGroup(mdl_lgst.dim)
mdl_lgst_target_cp = self.runSilent(pygsti.gaugeopt_to_target, mdl_clgst_cptp, self.model,
cptp_penalty_factor=1.0, gauge_group=TP_gauge_group, verbosity=10, checkJac=True)
mdl_lgst_tp = self.runSilent(pygsti.gaugeopt_to_target, mdl_lgst, None,
spam_penalty_factor=1.0, verbosity=10, checkJac=True)
mdl_lgst.basis = Basis.cast("gm",4) #so CPTP optimizations can work on mdl_lgst
mdl_lgst_cptp = self.runSilent(pygsti.gaugeopt_to_target, mdl_lgst, None,
cptp_penalty_factor=1.0, spam_penalty_factor=1.0, verbosity=10, checkJac=True)
mdl_lgst_cptp_tp = self.runSilent(pygsti.gaugeopt_to_target, mdl_lgst, None,
cptp_penalty_factor=1.0, spam_penalty_factor=1.0, gauge_group=TP_gauge_group, verbosity=10, checkJac=True) #no point? (remove?)
#I'm not sure why moving this test upward fixes a singlar matrix error (TODO LATER? - could one of above tests modify mdl_lgst??)
#mdl_lgst_tp = self.runSilent(pygsti.gaugeopt_to_target( mdl_lgst, None,
# spam_penalty_factor=1.0, verbosity=10, checkJac=True)
mdl_lgst_tptarget = self.runSilent(pygsti.gaugeopt_to_target, mdl_lgst, self.model,
spam_penalty_factor=1.0, verbosity=10, checkJac=True)
mdl_lgst_cptptarget = self.runSilent(pygsti.gaugeopt_to_target, mdl_lgst, self.model,
cptp_penalty_factor=1.0, spam_penalty_factor=1.0, verbosity=10, checkJac=True)
mdl_lgst_cptptarget2= self.runSilent(pygsti.gaugeopt_to_target, mdl_lgst, self.model,
cptp_penalty_factor=1.0, spam_penalty_factor=1.0, gauge_group=TP_gauge_group, verbosity=10, checkJac=True) #no point? (remove?)
#Use "None" gauge group
mdl_none = self.runSilent(pygsti.gaugeopt_to_target, mdl_lgst, self.model, gauge_group=None, verbosity=10)
soln, trivialEl, mdl_none = self.runSilent(pygsti.gaugeopt_to_target, mdl_lgst, self.model, gauge_group=None, verbosity=10, returnAll=True)
#Use "None" default gauge group
mdl_none = self.model.copy()
mdl_none.default_gauge_group = None
self.runSilent(pygsti.gaugeopt_to_target, mdl_none, self.model, verbosity=10)
soln, trivialEl, mdl_none = self.runSilent(pygsti.gaugeopt_to_target, mdl_none, self.model, verbosity=10, returnAll=True)
#TODO: check output lies in space desired
# big kick that should land it outside XP, TP, etc, so contraction
# routines are more tested
mdl_bigkick = mdl_lgst_target.kick(absmag=1.0)
mdl_badspam = mdl_bigkick.copy()
mdl_badspam.povms['Mdefault'] = pygsti.obj.UnconstrainedPOVM( [('0',np.array( [[2],[0],[0],[4]], 'd'))] )
#set a bad evec so vSPAM has to work...
mdl_clgst_tp = self.runSilent(pygsti.contract,mdl_bigkick, "TP", verbosity=10, tol=10.0)
mdl_clgst_cp = self.runSilent(pygsti.contract,mdl_bigkick, "CP", verbosity=10, tol=10.0)
mdl_clgst_cptp = self.runSilent(pygsti.contract,mdl_bigkick, "CPTP", verbosity=10, tol=10.0)
mdl_clgst_xp = self.runSilent(pygsti.contract,mdl_bigkick, "XP", ds, verbosity=10, tol=10.0)
mdl_clgst_xptp = self.runSilent(pygsti.contract,mdl_bigkick, "XPTP", ds, verbosity=10, tol=10.0)
mdl_clgst_vsp = self.runSilent(pygsti.contract,mdl_badspam, "vSPAM", verbosity=10, tol=10.0)
mdl_clgst_none = self.runSilent(pygsti.contract,mdl_bigkick, "nothing", verbosity=10, tol=10.0)
#TODO: check output lies in space desired
#Check Errors
with self.assertRaises(ValueError):
pygsti.contract(mdl_lgst_target, "FooBar",verbosity=0) # bad toWhat argument
def test_table_generation(self):
import pygsti.report.generation as gen
formats = ['latex','html','test','ppt'] #all the formats we know
tableclass = "cssClass"
longtable = False
confidenceRegionInfo = None
gateset = pygsti.io.load_gateset(compare_files + "/analysis.gateset")
ds = pygsti.objects.DataSet(fileToLoadFrom=compare_files + "/analysis.dataset")
chi2, chi2Hessian = pygsti.chi2(ds, gateset, returnHessian=True)
ci = pygsti.obj.ConfidenceRegion(gateset, chi2Hessian, 95.0,
hessianProjection="std")
gateset_tp = pygsti.contract(gateset,"TP"); gateset_tp.set_all_parameterizations("TP")
chi2, chi2Hessian_TP = pygsti.chi2(ds, gateset_tp, returnHessian=True)
ci_TP = pygsti.obj.ConfidenceRegion(gateset_tp, chi2Hessian_TP, 95.0,
hessianProjection="std")
chi2, chi2Hessian_tgt = pygsti.chi2(ds, std.gs_target, returnHessian=True)
ci_tgt = pygsti.obj.ConfidenceRegion(std.gs_target, chi2Hessian_tgt, 95.0,
hessianProjection="std")
target_tp = std.gs_target.copy(); target_tp.set_all_parameterizations("TP")
chi2, chi2Hessian_tgt_TP = pygsti.chi2(ds, target_tp, returnHessian=True)
ci_TP_tgt = pygsti.obj.ConfidenceRegion(target_tp, chi2Hessian_tgt_TP, 95.0,
hessianProjection="std")
gateset_2q = pygsti.construction.build_gateset(
[4], [('Q0','Q1')],['GIX','GIY','GXI','GYI','GCNOT'],
[ "I(Q0):X(pi/2,Q1)", "I(Q0):Y(pi/2,Q1)", "X(pi/2,Q0):I(Q1)", "Y(pi/2,Q0):I(Q1)", "CX(pi,Q0,Q1)" ],
prepLabels=['rho0'], prepExpressions=["0"], effectLabels=['E0','E1','E2'], effectExpressions=["0","1","2"],
spamdefs={'upup': ('rho0','E0'), 'updn': ('rho0','E1'), 'dnup': ('rho0','E2'),
'dndn': ('rho0','remainder') }, basis="pp" )
#tests which fill in the cracks of the full-report tests
tab = gen.get_gateset_spam_table(gateset, None)
tab_wCI = gen.get_gateset_spam_table(gateset, ci)
table_wCI_as_str = str(tab_wCI)
gen.get_gateset_spam_table(gateset, None)
gen.get_gateset_gates_table(gateset_tp, ci_TP) #test zero-padding
gen.get_unitary_gateset_gates_table(std.gs_target, ci_tgt) #unitary gates w/CIs
gen.get_unitary_gateset_gates_table(target_tp, ci_TP_tgt) #unitary gates w/CIs
gen.get_gateset_closest_unitary_table(gateset_2q, None) #test higher-dim gateset
gen.get_gateset_closest_unitary_table(gateset, ci) #test with CIs (long...)
gen.get_gateset_rotn_axis_table(std.gs_target, None) #try to get "--"s and "X"s to display
gen.get_chi2_progress_table([0], [gateset_tp], [ [('Gx',)],], ds) #TP case
gen.get_chi2_confidence_region(gateset_tp, ds, 95) #TP case
gen.get_gatestring_multi_table([ [('Gx',),('Gz',)], [('Gy',)] ],
["list1","list2"], commonTitle=None) #commonTitle == None case w/diff length lists
with self.assertRaises(ValueError):
gen.get_unitary_gateset_gates_table(std.gs_target, ci) #gateset-CI mismatch
with self.assertRaises(ValueError):
gen.get_gateset_spam_parameters_table(std.gs_target, ci) #gateset-CI mismatch
#LogL case tests
gen.get_logl_progress_table([0], [gateset_tp], [ [('Gx',)],], ds) # logL case
gen.get_logl_progress_table([0], [gateset], [ [('Gx',)],], ds) # logL case
gen.get_logl_confidence_region(gateset_tp, ds, 95,
gatestring_list=None, probClipInterval=(-1e6,1e6),
minProbClip=1e-4, radius=1e-4, hessianProjection="std")
gen.get_logl_confidence_region(gateset, ds, 95,
gatestring_list=None, probClipInterval=(-1e6,1e6),
minProbClip=1e-4, radius=1e-4, hessianProjection="std")
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_eLGST(self):
ds = self.ds
assert (pygsti.obj.Model._pcheck)
mdl_lgst = pygsti.do_lgst(ds,
self.fiducials,
self.fiducials,
self.model,
svdTruncateTo=4,
verbosity=0)
#mdl_lgst._check_paramvec() #will fail, but OK, since paramvec is computed only when *needed* now
mdl_lgst_go = pygsti.gaugeopt_to_target(mdl_lgst,
self.model, {
'spam': 1.0,
'gates': 1.0
},
checkJac=True)
mdl_lgst_go._check_paramvec()
mdl_clgst = pygsti.contract(mdl_lgst_go, "CPTP")
mdl_clgst.to_vector() # to make sure we're in sync
mdl_clgst._check_paramvec()
self.model._check_paramvec()
_, mdl_single_exlgst = pygsti.do_exlgst(ds,
mdl_clgst,
self.elgstStrings[0],
self.fiducials,
self.fiducials,
self.model,
regularizeFactor=1e-3,
svdTruncateTo=4,
verbosity=0)
mdl_single_exlgst._check_paramvec()
_, mdl_single_exlgst_verb = self.runSilent(pygsti.do_exlgst,
ds,
mdl_clgst,
self.elgstStrings[0],
self.fiducials,
self.fiducials,
self.model,
regularizeFactor=1e-3,
svdTruncateTo=4,
verbosity=10)
mdl_single_exlgst_verb._check_paramvec()
self.assertAlmostEqual(
mdl_single_exlgst.frobeniusdist(mdl_single_exlgst_verb), 0)
mdl_exlgst = pygsti.do_iterative_exlgst(ds,
mdl_clgst,
self.fiducials,
self.fiducials,
self.elgstStrings,
targetModel=self.model,
svdTruncateTo=4,
verbosity=0)
all_minErrs, all_gs_exlgst_tups = pygsti.do_iterative_exlgst(
ds,
mdl_clgst,
self.fiducials,
self.fiducials,
[[mdl.tup for mdl in gsList] for gsList in self.elgstStrings],
targetModel=self.model,
svdTruncateTo=4,
verbosity=0,
returnAll=True,
returnErrorVec=True)
mdl_exlgst_verb = self.runSilent(pygsti.do_iterative_exlgst,
ds,
mdl_clgst,
self.fiducials,
self.fiducials,
self.elgstStrings,
targetModel=self.model,
svdTruncateTo=4,
verbosity=10)
mdl_exlgst_reg = pygsti.do_iterative_exlgst(ds,
mdl_clgst,
self.fiducials,
self.fiducials,
self.elgstStrings,
targetModel=self.model,
svdTruncateTo=4,
verbosity=0,
regularizeFactor=10)
self.assertAlmostEqual(mdl_exlgst.frobeniusdist(mdl_exlgst_verb), 0)
self.assertAlmostEqual(
mdl_exlgst.frobeniusdist(all_gs_exlgst_tups[-1]), 0)
#Run internal checks on less max-L values (so it doesn't take forever)
mdl_exlgst_chk = pygsti.do_iterative_exlgst(ds,
mdl_clgst,
self.fiducials,
self.fiducials,
self.elgstStrings[0:2],
targetModel=self.model,
svdTruncateTo=4,
verbosity=0,
check_jacobian=True)
mdl_exlgst_chk_verb = self.runSilent(pygsti.do_iterative_exlgst,
ds,
mdl_clgst,
self.fiducials,
self.fiducials,
self.elgstStrings[0:2],
targetModel=self.model,
svdTruncateTo=4,
verbosity=10,
check_jacobian=True)
# RUN BELOW LINES TO SEED SAVED GATESET FILES
if regenerate_references():
pygsti.io.write_model(mdl_exlgst, compare_files + "/exlgst.model",
"Saved Extended-LGST (eLGST) Model")
pygsti.io.write_model(
mdl_exlgst_reg, compare_files + "/exlgst_reg.model",
"Saved Extended-LGST (eLGST) Model w/regularization")
mdl_exlgst_compare = pygsti.io.load_model(compare_files +
"/exlgst.model")
mdl_exlgst_reg_compare = pygsti.io.load_model(compare_files +
"/exlgst_reg.model")
mdl_exlgst.set_all_parameterizations(
"full"
) # b/c ex-LGST sets spam to StaticSPAMVec objects (b/c they're not optimized)
mdl_exlgst_reg.set_all_parameterizations(
"full"
) # b/c ex-LGST sets spam to StaticSPAMVec objects (b/c they're not optimized)
mdl_exlgst_go = pygsti.gaugeopt_to_target(mdl_exlgst,
mdl_exlgst_compare,
{'spam': 1.0},
checkJac=True)
mdl_exlgst_reg_go = pygsti.gaugeopt_to_target(mdl_exlgst_reg,
mdl_exlgst_reg_compare,
{'spam': 1.0},
checkJac=True)
def test_LGST(self):
ds = self.ds
#pygsti.construction.generate_fake_data(self.datagen_gateset, self.lgstStrings, nSamples=1000,
# sampleError='binomial', seed=None)
print("GG0 = ",self.model.default_gauge_group)
mdl_lgst = pygsti.do_lgst(ds, self.fiducials, self.fiducials, self.model, svdTruncateTo=4, verbosity=0)
mdl_lgst_verb = self.runSilent(pygsti.do_lgst, ds, self.fiducials, self.fiducials, self.model, svdTruncateTo=4, verbosity=10)
self.assertAlmostEqual(mdl_lgst.frobeniusdist(mdl_lgst_verb),0)
print("GG = ",mdl_lgst.default_gauge_group)
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")
# 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_lgst,compare_files + "/lgst.model", "Saved LGST Model before gauge optimization")
pygsti.io.write_model(mdl_lgst_go,compare_files + "/lgst_go.model", "Saved LGST Model after gauge optimization")
pygsti.io.write_model(mdl_clgst,compare_files + "/clgst.model", "Saved LGST Model after G.O. and CPTP contraction")
mdl_lgst_compare = pygsti.io.load_model(compare_files + "/lgst.model")
mdl_lgst_go_compare = pygsti.io.load_model(compare_files + "/lgst_go.model")
mdl_clgst_compare = pygsti.io.load_model(compare_files + "/clgst.model")
self.assertAlmostEqual( mdl_lgst.frobeniusdist(mdl_lgst_compare), 0, places=5)
self.assertAlmostEqual( mdl_lgst_go.frobeniusdist(mdl_lgst_go_compare), 0, places=5)
self.assertAlmostEqual( mdl_clgst.frobeniusdist(mdl_clgst_compare), 0, places=5)
#Check for error conditions
with self.assertRaises(ValueError):
mdl_lgst = pygsti.do_lgst(ds, self.fiducials, self.fiducials, None, svdTruncateTo=4, verbosity=0) #no target model
with self.assertRaises(ValueError):
mdl_lgst = pygsti.do_lgst(ds, self.fiducials, self.fiducials, None, opLabels=list(self.model.operations.keys()),
svdTruncateTo=4, verbosity=0) #no spam dict
#No need for identity vector anymore
#with self.assertRaises(ValueError):
# mdl_lgst = pygsti.do_lgst(ds, self.fiducials, self.fiducials, None, opLabels=list(self.model.operations.keys()),
# spamDict=self.model.get_reverse_spam_defs(),
# svdTruncateTo=4, verbosity=0) #no identity vector
with self.assertRaises(ValueError):
bad_fids =pygsti.construction.circuit_list([('Gx',),('Gx',),('Gx',),('Gx',)])
mdl_lgst = pygsti.do_lgst(ds, bad_fids, bad_fids, self.model, svdTruncateTo=4, verbosity=0) # bad fiducials (rank deficient)
with self.assertRaises(KeyError): # AB-matrix construction error
incomplete_strings = self.lgstStrings[5:] #drop first 5 strings...
bad_ds = pygsti.construction.generate_fake_data(
self.datagen_gateset, incomplete_strings,
nSamples=10, sampleError='none')
mdl_lgst = pygsti.do_lgst(bad_ds, self.fiducials, self.fiducials, self.model,
svdTruncateTo=4, verbosity=0)
# incomplete dataset
with self.assertRaises(KeyError): # X-matrix construction error
incomplete_strings = self.lgstStrings[:-5] #drop last 5 strings...
bad_ds = pygsti.construction.generate_fake_data(
self.datagen_gateset, incomplete_strings,
nSamples=10, sampleError='none')
mdl_lgst = pygsti.do_lgst(bad_ds, self.fiducials, self.fiducials, self.model,
svdTruncateTo=4, verbosity=0)
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 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 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
target_model = std.target_model()
datagen_gateset = target_model.depolarize(op_noise=0.05, spam_noise=0.1)
datagen_gateset2 = target_model.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
op_labels = std.gates
cls.lgstStrings = pygsti.circuits.create_lgst_circuits(std.fiducials, std.fiducials, op_labels)
cls.maxLengthList = [1,2,4,8]
cls.lsgstStrings = pygsti.circuits.create_lsgst_circuit_lists(
op_labels, std.fiducials, std.fiducials, std.germs, cls.maxLengthList)
cls.lsgstStructs = pygsti.circuits.make_lsgst_structs(
op_labels, std.fiducials, std.fiducials, std.germs, cls.maxLengthList)
# RUN BELOW LINES TO GENERATE ANALYSIS DATASET (SAVE)
if regenerate_references():
ds = pygsti.data.simulate_data(datagen_gateset, cls.lsgstStrings[-1], num_samples=1000,
sample_error='binomial', seed=100)
ds.save(compare_files + "/reportgen.dataset")
ds2 = pygsti.data.simulate_data(datagen_gateset2, cls.lsgstStrings[-1], num_samples=1000,
sample_error='binomial', seed=100)
ds2.save(compare_files + "/reportgen2.dataset")
cls.ds = pygsti.data.DataSet(file_to_load_from=compare_files + "/reportgen.dataset")
cls.ds2 = pygsti.data.DataSet(file_to_load_from=compare_files + "/reportgen2.dataset")
mdl_lgst = pygsti.run_lgst(cls.ds, std.fiducials, std.fiducials, target_model, svd_truncate_to=4, verbosity=0)
mdl_lgst_go = pygsti.gaugeopt_to_target(mdl_lgst, target_model, {'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("full TP")
#Compute results for MC2GST
lsgst_gatesets_prego, *_ = pygsti.run_iterative_gst(
cls.ds, cls.mdl_clgst, cls.lsgstStrings,
optimizer={'tol': 1e-5},
iteration_objfn_builders=['chi2'],
final_objfn_builders=[],
resource_alloc=None,
verbosity=0
)
experiment_design = pygsti.protocols.StandardGSTDesign(
target_model.create_processor_spec(), std.fiducials, std.fiducials, std.germs, cls.maxLengthList
)
data = pygsti.protocols.ProtocolData(experiment_design, cls.ds)
protocol = pygsti.protocols.StandardGST()
cls.results = pygsti.protocols.gst.ModelEstimateResults(data, protocol)
cls.results.add_estimate(pygsti.protocols.estimate.Estimate.create_gst_estimate(
cls.results, target_model, cls.mdl_clgst,lsgst_gatesets_prego,
{'objective': "chi2",
'min_prob_clip_for_weighting': 1e-4,
'prob_clip_interval': (-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
('target_model', target_model), #so can gauge-propagate CIs
('cptp_penalty_factor', 0),
('gates_metric',"frobenius"),
('spam_metric',"frobenius"),
('item_weights', {'gates': 1.0, 'spam': 0.001}),
('return_all', 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 run_long_sequence_gst with a non-mark dataset to trigger data scaling
tp_target = target_model.copy(); tp_target.set_all_parameterizations("full TP")
cls.ds3 = cls.ds.copy_nonstatic()
cls.ds3.add_counts_from_dataset(cls.ds2)
cls.ds3.done_adding_data()
cls.results_logL = pygsti.run_long_sequence_gst(cls.ds3, tp_target, std.fiducials, std.fiducials,
std.germs, cls.maxLengthList, verbosity=0,
advanced_options={'tolerance': 1e-6, 'starting_point': 'LGST',
'on_bad_fit': ["robust","Robust","robust+","Robust+"],
'bad_fit_threshold': -1.0,
'germ_length_limits': {('Gx','Gi','Gi'): 2} })
#OLD
#lsgst_gatesets_TP = pygsti.do_iterative_mlgst(cls.ds, cls.mdl_clgst_tp, cls.lsgstStrings, verbosity=0,
# min_prob_clip=1e-4, prob_clip_interval=(-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(target_model, cls.mdl_clgst_tp,
# lsgst_gatesets_TP,
# {'objective': "logl",
# 'min_prob_clip': 1e-4,
# 'prob_clip_interval': (-1e6,1e6), 'radius': 1e-4,
# 'weights': None, 'defaultDirectory': temp_files + "",
# 'defaultBasename': "MyDefaultReportName"})
#
#tp_target = target_model.copy(); tp_target.set_all_parameterizations("full TP")
#gaugeOptParams = gaugeOptParams.copy() #just to be safe
#gaugeOptParams['model'] = lsgst_gatesets_TP[-1] #so can gauge-propagate CIs
#gaugeOptParams['target_model'] = 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_table_generation(self):
import pygsti.report.generation as gen
formats = ['latex', 'html', 'py', 'ppt'] #all the formats we know
tableclass = "cssClass"
longtable = False
confidenceRegionInfo = None
gateset = pygsti.io.load_gateset("cmp_chk_files/analysis.gateset")
ds = pygsti.objects.DataSet(
fileToLoadFrom="cmp_chk_files/analysis.dataset")
chi2, chi2Hessian = pygsti.chi2(ds, gateset, returnHessian=True)
ci = pygsti.obj.ConfidenceRegion(gateset,
chi2Hessian,
95.0,
hessianProjection="std")
gateset_tp = pygsti.contract(gateset, "TP")
gateset_tp.set_all_parameterizations("TP")
chi2, chi2Hessian_TP = pygsti.chi2(ds, gateset_tp, returnHessian=True)
ci_TP = pygsti.obj.ConfidenceRegion(gateset_tp,
chi2Hessian_TP,
95.0,
hessianProjection="std")
chi2, chi2Hessian_tgt = pygsti.chi2(ds,
std.gs_target,
returnHessian=True)
ci_tgt = pygsti.obj.ConfidenceRegion(std.gs_target,
chi2Hessian_tgt,
95.0,
hessianProjection="std")
target_tp = std.gs_target.copy()
target_tp.set_all_parameterizations("TP")
chi2, chi2Hessian_tgt_TP = pygsti.chi2(ds,
target_tp,
returnHessian=True)
ci_TP_tgt = pygsti.obj.ConfidenceRegion(target_tp,
chi2Hessian_tgt_TP,
95.0,
hessianProjection="std")
gateset_2q = pygsti.construction.build_gateset(
[4], [('Q0', 'Q1')], ['GIX', 'GIY', 'GXI', 'GYI', 'GCNOT'], [
"I(Q0):X(pi/2,Q1)", "I(Q0):Y(pi/2,Q1)", "X(pi/2,Q0):I(Q1)",
"Y(pi/2,Q0):I(Q1)", "CX(pi,Q0,Q1)"
],
prepLabels=['rho0'],
prepExpressions=["0"],
effectLabels=['E0', 'E1', 'E2'],
effectExpressions=["0", "1", "2"],
spamdefs={
'upup': ('rho0', 'E0'),
'updn': ('rho0', 'E1'),
'dnup': ('rho0', 'E2'),
'dndn': ('rho0', 'remainder')
},
basis="pp")
#tests which fill in the cracks of the full-report tests
tab = gen.get_gateset_spam_table(gateset, None)
tab_wCI = gen.get_gateset_spam_table(gateset, ci)
gen.get_gateset_spam_table(gateset, None)
gen.get_gateset_gates_table(gateset_tp, ci_TP) #test zero-padding
gen.get_unitary_gateset_gates_table(std.gs_target,
ci_tgt) #unitary gates w/CIs
gen.get_unitary_gateset_gates_table(target_tp,
ci_TP_tgt) #unitary gates w/CIs
gen.get_gateset_closest_unitary_table(gateset_2q,
None) #test higher-dim gateset
gen.get_gateset_closest_unitary_table(gateset,
ci) #test with CIs (long...)
gen.get_gateset_rotn_axis_table(
std.gs_target, None) #try to get "--"s and "X"s to display
gen.get_chi2_progress_table([0], [gateset_tp], [
[('Gx', )],
], ds) #TP case
gen.get_chi2_confidence_region(gateset_tp, ds, 95) #TP case
gen.get_gatestring_multi_table(
[[('Gx', ), ('Gz', )], [('Gy', )]], ["list1", "list2"],
commonTitle=None) #commonTitle == None case w/diff length lists
with self.assertRaises(ValueError):
gen.get_unitary_gateset_gates_table(std.gs_target,
ci) #gateset-CI mismatch
with self.assertRaises(ValueError):
gen.get_gateset_spam_parameters_table(std.gs_target,
ci) #gateset-CI mismatch
#Test ReportTable object
rowLabels = tab.keys()
row1Data = tab[rowLabels[0]]
colLabels = row1Data.keys()
self.assertTrue(rowLabels, tab.row_names)
self.assertTrue(colLabels, tab.col_names)
self.assertTrue(len(rowLabels), tab.num_rows)
self.assertTrue(len(colLabels), tab.num_cols)
el00 = tab[rowLabels[0]][colLabels[0]]
self.assertTrue(rowLabels[0] in tab)
self.assertTrue(tab.has_key(rowLabels[0]))
table_len = len(tab)
self.assertEqual(table_len, tab.num_rows)
table_as_str = str(tab)
table_wCI_as_str = str(tab_wCI)
row1a = tab.row(key=rowLabels[0])
col1a = tab.col(key=colLabels[0])
row1b = tab.row(index=0)
col1b = tab.col(index=0)
self.assertEqual(row1a, row1b)
self.assertEqual(col1a, col1b)
with self.assertRaises(KeyError):
tab['foobar']
with self.assertRaises(KeyError):
tab.row(key='foobar') #invalid key
with self.assertRaises(ValueError):
tab.row(index=100000) #out of bounds
with self.assertRaises(ValueError):
tab.row() #must specify key or index
with self.assertRaises(ValueError):
tab.row(key='foobar', index=1) #cannot specify key and index
with self.assertRaises(KeyError):
tab.col(key='foobar') #invalid key
with self.assertRaises(ValueError):
tab.col(index=100000) #out of bounds
with self.assertRaises(ValueError):
tab.col() #must specify key or index
with self.assertRaises(ValueError):
tab.col(key='foobar', index=1) #cannot specify key and index
with self.assertRaises(ValueError):
tab.render(fmt="foobar") #invalid format
#LogL case tests
gen.get_logl_progress_table([0], [gateset_tp], [
[('Gx', )],
], ds) # logL case
gen.get_logl_progress_table([0], [gateset], [
[('Gx', )],
], ds) # logL case
gen.get_logl_confidence_region(gateset_tp,
ds,
95,
gatestring_list=None,
probClipInterval=(-1e6, 1e6),
minProbClip=1e-4,
radius=1e-4,
hessianProjection="std")
gen.get_logl_confidence_region(gateset,
ds,
95,
gatestring_list=None,
probClipInterval=(-1e6, 1e6),
minProbClip=1e-4,
radius=1e-4,
hessianProjection="std")
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 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_gaugeopt_and_contract(self):
ds = self.ds_lgst
#pygsti.construction.generate_fake_data(self.datagen_gateset, self.lgstStrings,
# nSamples=10000,sampleError='binomial', seed=100)
gs_lgst = pygsti.do_lgst(ds, self.specs, self.gateset, svdTruncateTo=4, verbosity=0)
#Gauge Opt to Target
gs_lgst_target = self.runSilent(pygsti.optimize_gauge, gs_lgst,"target",targetGateset=self.gateset,verbosity=10)
#Gauge Opt to Target using non-frobenius metrics
gs_lgst_targetAlt = self.runSilent(pygsti.optimize_gauge, gs_lgst_target,"target",targetGateset=self.gateset,
targetGatesMetric='fidelity', verbosity=10)
gs_lgst_targetAlt = self.runSilent(pygsti.optimize_gauge, gs_lgst_target,"target",targetGateset=self.gateset,
targetGatesMetric='tracedist', verbosity=10)
gs_lgst_targetAlt = self.runSilent(pygsti.optimize_gauge, gs_lgst_target,"target",targetGateset=self.gateset,
targetSpamMetric='fidelity', verbosity=10)
gs_lgst_targetAlt = self.runSilent(pygsti.optimize_gauge, gs_lgst_target,"target",targetGateset=self.gateset,
targetSpamMetric='tracedist', verbosity=10)
with self.assertRaises(ValueError):
self.runSilent(pygsti.optimize_gauge, gs_lgst_target,"target",targetGateset=self.gateset,
targetGatesMetric='foobar', verbosity=10) #bad targetGatesMetric
with self.assertRaises(ValueError):
self.runSilent(pygsti.optimize_gauge, gs_lgst_target,"target",targetGateset=self.gateset,
targetSpamMetric='foobar', verbosity=10) #bad targetSpamMetric
with self.assertRaises(ValueError):
self.runSilent(pygsti.optimize_gauge, gs_lgst_target,"foobar",targetGateset=self.gateset,
targetSpamMetric='target', verbosity=10) #bad toGetTo
#Contractions
gs_clgst_tp = self.runSilent(pygsti.contract, gs_lgst_target, "TP",verbosity=10, tol=10.0)
gs_clgst_cp = self.runSilent(pygsti.contract, gs_lgst_target, "CP",verbosity=10, tol=10.0)
gs_clgst_cptp = self.runSilent(pygsti.contract, gs_lgst_target, "CPTP",verbosity=10, tol=10.0)
gs_clgst_cptp2 = self.runSilent(pygsti.contract, gs_lgst_target, "CPTP",verbosity=10, useDirectCP=False)
gs_clgst_cptp3 = self.runSilent(pygsti.contract, gs_lgst_target, "CPTP",verbosity=10, tol=10.0, maxiter=0)
gs_clgst_xp = self.runSilent(pygsti.contract, gs_lgst_target, "XP", ds,verbosity=10, tol=10.0)
gs_clgst_xptp = self.runSilent(pygsti.contract, gs_lgst_target, "XPTP", ds,verbosity=10, tol=10.0)
gs_clgst_vsp = self.runSilent(pygsti.contract, gs_lgst_target, "vSPAM",verbosity=10, tol=10.0)
gs_clgst_none = self.runSilent(pygsti.contract, gs_lgst_target, "nothing",verbosity=10, tol=10.0)
#test bad effect vector cases
gs_bad_effect = gs_lgst_target.copy()
gs_bad_effect.effects['E0'] = [100.0,0,0,0] # E eigvals all > 1.0
self.runSilent(pygsti.contract, gs_bad_effect, "vSPAM",verbosity=10, tol=10.0)
gs_bad_effect.effects['E0'] = [-100.0,0,0,0] # E eigvals all < 0
self.runSilent(pygsti.contract, gs_bad_effect, "vSPAM",verbosity=10, tol=10.0)
with self.assertRaises(ValueError):
self.runSilent(pygsti.contract, gs_lgst_target, "foobar",verbosity=10, tol=10.0) #bad toWhat
#More gauge optimizations
gs_lgst_target_cp = self.runSilent(pygsti.optimize_gauge, gs_clgst_cptp,"target",targetGateset=self.gateset,
constrainToCP=True,constrainToTP=True,constrainToValidSpam=True,verbosity=10)
gs_lgst_cptp = self.runSilent(pygsti.optimize_gauge, gs_lgst,"CPTP",verbosity=10)
gs_lgst_cptp_tp = self.runSilent(pygsti.optimize_gauge, gs_lgst,"CPTP",verbosity=10, constrainToTP=True)
gs_lgst_tp = self.runSilent(pygsti.optimize_gauge, gs_lgst,"TP",verbosity=10)
gs_lgst_tptarget = self.runSilent(pygsti.optimize_gauge, gs_lgst,"TP and target",targetGateset=self.gateset,verbosity=10)
gs_lgst_cptptarget = self.runSilent(pygsti.optimize_gauge, gs_lgst,"CPTP and target",targetGateset=self.gateset,verbosity=10)
gs_lgst_cptptarget2= self.runSilent(pygsti.optimize_gauge, gs_lgst,"CPTP and target",targetGateset=self.gateset,
verbosity=10, constrainToTP=True)
gs_lgst_cd = self.runSilent(pygsti.optimize_gauge, gs_lgst,"Completely Depolarized",targetGateset=self.gateset,verbosity=10)
#TODO: check output lies in space desired
# big kick that should land it outside XP, TP, etc, so contraction
# routines are more tested
gs_bigkick = gs_lgst_target.kick(absmag=1.0)
gs_badspam = gs_bigkick.copy()
gs_badspam.effects['E0'] = np.array( [[2],[0],[0],[4]], 'd') #set a bad evec so vSPAM has to work...
gs_clgst_tp = self.runSilent(pygsti.contract,gs_bigkick, "TP", verbosity=10, tol=10.0)
gs_clgst_cp = self.runSilent(pygsti.contract,gs_bigkick, "CP", verbosity=10, tol=10.0)
gs_clgst_cptp = self.runSilent(pygsti.contract,gs_bigkick, "CPTP", verbosity=10, tol=10.0)
gs_clgst_xp = self.runSilent(pygsti.contract,gs_bigkick, "XP", ds, verbosity=10, tol=10.0)
gs_clgst_xptp = self.runSilent(pygsti.contract,gs_bigkick, "XPTP", ds, verbosity=10, tol=10.0)
gs_clgst_vsp = self.runSilent(pygsti.contract,gs_badspam, "vSPAM", verbosity=10, tol=10.0)
gs_clgst_none = self.runSilent(pygsti.contract,gs_bigkick, "nothing", verbosity=10, tol=10.0)
#TODO: check output lies in space desired
#Check Errors
with self.assertRaises(ValueError):
pygsti.optimize_gauge(gs_lgst,"FooBar",verbosity=0) # bad toGetTo argument
with self.assertRaises(ValueError):
pygsti.contract(gs_lgst_target, "FooBar",verbosity=0) # bad toWhat argument
def test_table_generation(self):
import pygsti.report.generation as gen
formats = ['latex','html','py','ppt'] #all the formats we know
tableclass = "cssClass"
longtable = False
confidenceRegionInfo = None
gateset = pygsti.io.load_gateset("cmp_chk_files/analysis.gateset")
ds = pygsti.objects.DataSet(fileToLoadFrom="cmp_chk_files/analysis.dataset")
chi2, chi2Hessian = pygsti.chi2(ds, gateset, returnHessian=True)
ci = pygsti.obj.ConfidenceRegion(gateset, chi2Hessian, 95.0,
hessianProjection="std")
gateset_tp = pygsti.contract(gateset,"TP"); gateset_tp.set_all_parameterizations("TP")
chi2, chi2Hessian_TP = pygsti.chi2(ds, gateset_tp, returnHessian=True)
ci_TP = pygsti.obj.ConfidenceRegion(gateset_tp, chi2Hessian_TP, 95.0,
hessianProjection="std")
chi2, chi2Hessian_tgt = pygsti.chi2(ds, std.gs_target, returnHessian=True)
ci_tgt = pygsti.obj.ConfidenceRegion(std.gs_target, chi2Hessian_tgt, 95.0,
hessianProjection="std")
target_tp = std.gs_target.copy(); target_tp.set_all_parameterizations("TP")
chi2, chi2Hessian_tgt_TP = pygsti.chi2(ds, target_tp, returnHessian=True)
ci_TP_tgt = pygsti.obj.ConfidenceRegion(target_tp, chi2Hessian_tgt_TP, 95.0,
hessianProjection="std")
gateset_2q = pygsti.construction.build_gateset(
[4], [('Q0','Q1')],['GIX','GIY','GXI','GYI','GCNOT'],
[ "I(Q0):X(pi/2,Q1)", "I(Q0):Y(pi/2,Q1)", "X(pi/2,Q0):I(Q1)", "Y(pi/2,Q0):I(Q1)", "CX(pi,Q0,Q1)" ],
prepLabels=['rho0'], prepExpressions=["0"], effectLabels=['E0','E1','E2'], effectExpressions=["0","1","2"],
spamdefs={'upup': ('rho0','E0'), 'updn': ('rho0','E1'), 'dnup': ('rho0','E2'),
'dndn': ('rho0','remainder') }, basis="pp" )
#tests which fill in the cracks of the full-report tests
with self.assertRaises(ValueError):
gen.get_gateset_spam_table(gateset, formats, tableclass,
longtable, None, mxBasis="fooBar")
tab = gen.get_gateset_spam_table(gateset, formats, tableclass, longtable, None, mxBasis="gm")
tab_wCI = gen.get_gateset_spam_table(gateset, formats, tableclass, longtable, ci, mxBasis="gm")
gen.get_gateset_spam_table(gateset, formats, tableclass, longtable, None, mxBasis="std")
gen.get_gateset_spam_table(gateset, formats, tableclass, longtable, None, mxBasis="pp")
gen.get_gateset_gates_table(gateset_tp, formats, tableclass, longtable, ci_TP) #test zero-padding
gen.get_unitary_gateset_gates_table(std.gs_target, formats, tableclass, longtable, ci_tgt) #unitary gates w/CIs
gen.get_unitary_gateset_gates_table(target_tp, formats, tableclass, longtable, ci_TP_tgt) #unitary gates w/CIs
gen.get_gateset_closest_unitary_table(gateset_2q, formats, tableclass, longtable, None) #test higher-dim gateset
gen.get_gateset_closest_unitary_table(gateset, formats, tableclass, longtable, ci) #test with CIs (long...)
gen.get_gateset_rotn_axis_table(std.gs_target, formats, tableclass, longtable, None) #try to get "--"s and "X"s to display
gen.get_chi2_progress_table([0], [gateset_tp], [ [('Gx',)],], ds, formats, tableclass, longtable) #TP case
gen.get_chi2_confidence_region(gateset_tp, ds, 95) #TP case
gen.get_gatestring_multi_table([ [('Gx',),('Gz',)], [('Gy',)] ], ["list1","list2"], formats,
tableclass, longtable, commonTitle=None) #commonTitle == None case w/diff length lists
with self.assertRaises(ValueError):
gen.get_unitary_gateset_gates_table(std.gs_target, formats, tableclass, longtable, ci) #gateset-CI mismatch
with self.assertRaises(ValueError):
gen.get_gateset_spam_parameters_table(std.gs_target, formats, tableclass, longtable, ci) #gateset-CI mismatch
#Test ReportTable object
rowLabels = tab.keys()
row1Data = tab[rowLabels[0]]
colLabels = row1Data.keys()
self.assertTrue(rowLabels, tab.row_names)
self.assertTrue(colLabels, tab.col_names)
self.assertTrue(len(rowLabels), tab.num_rows)
self.assertTrue(len(colLabels), tab.num_cols)
el00 = tab[rowLabels[0]][colLabels[0]]
self.assertTrue( rowLabels[0] in tab )
self.assertTrue( tab.has_key(rowLabels[0]) )
table_len = len(tab)
self.assertEqual(table_len, tab.num_rows)
table_as_str = str(tab)
table_wCI_as_str = str(tab_wCI)
row1a = tab.row(key=rowLabels[0])
col1a = tab.col(key=colLabels[0])
row1b = tab.row(index=0)
col1b = tab.col(index=0)
self.assertEqual(row1a,row1b)
self.assertEqual(col1a,col1b)
with self.assertRaises(KeyError):
tab['foobar']
with self.assertRaises(KeyError):
tab.row(key='foobar') #invalid key
with self.assertRaises(ValueError):
tab.row(index=100000) #out of bounds
with self.assertRaises(ValueError):
tab.row() #must specify key or index
with self.assertRaises(ValueError):
tab.row(key='foobar',index=1) #cannot specify key and index
with self.assertRaises(KeyError):
tab.col(key='foobar') #invalid key
with self.assertRaises(ValueError):
tab.col(index=100000) #out of bounds
with self.assertRaises(ValueError):
tab.col() #must specify key or index
with self.assertRaises(ValueError):
tab.col(key='foobar',index=1) #cannot specify key and index
with self.assertRaises(ValueError):
tab.render(fmt="foobar") #invalid format
latexOnlyTab = pygsti.report.table.ReportTable(['latex'], ['Col1','Col2'], (None,None),
"tableClass",False)
with self.assertRaises(KeyError):
x = str(latexOnlyTab) #must have 'py' format
#LogL case tests
gen.get_logl_progress_table([0], [gateset_tp], [ [('Gx',)],], ds, formats, tableclass, longtable) # logL case
gen.get_logl_progress_table([0], [gateset], [ [('Gx',)],], ds, formats, tableclass, longtable) # logL case
gen.get_logl_confidence_region(gateset_tp, ds, 95,
gatestring_list=None, probClipInterval=(-1e6,1e6),
minProbClip=1e-4, radius=1e-4, hessianProjection="std")
gen.get_logl_confidence_region(gateset, ds, 95,
gatestring_list=None, probClipInterval=(-1e6,1e6),
minProbClip=1e-4, radius=1e-4, hessianProjection="std")
def mdl_clgst(self):
return pygsti.contract(self.mdl_lgst_go, "CPTP")
def test_eLGST(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_exlgst = pygsti.do_exlgst(ds,
gs_clgst,
self.elgstStrings[0],
self.specs,
self.gateset,
regularizeFactor=1e-3,
svdTruncateTo=4,
verbosity=0)
gs_single_exlgst_verb = self.runSilent(pygsti.do_exlgst,
ds,
gs_clgst,
self.elgstStrings[0],
self.specs,
self.gateset,
regularizeFactor=1e-3,
svdTruncateTo=4,
verbosity=10)
gs_exlgst = pygsti.do_iterative_exlgst(ds,
gs_clgst,
self.specs,
self.elgstStrings,
targetGateset=self.gateset,
svdTruncateTo=4,
verbosity=0)
all_minErrs, all_gs_exlgst_tups = pygsti.do_iterative_exlgst(
ds,
gs_clgst,
self.specs,
[[gs.tup for gs in gsList] for gsList in self.elgstStrings],
targetGateset=self.gateset,
svdTruncateTo=4,
verbosity=0,
returnAll=True,
returnErrorVec=True)
gs_exlgst_verb = self.runSilent(pygsti.do_iterative_exlgst,
ds,
gs_clgst,
self.specs,
self.elgstStrings,
targetGateset=self.gateset,
svdTruncateTo=4,
verbosity=10)
gs_exlgst_reg = pygsti.do_iterative_exlgst(ds,
gs_clgst,
self.specs,
self.elgstStrings,
targetGateset=self.gateset,
svdTruncateTo=4,
verbosity=0,
regularizeFactor=10)
self.assertAlmostEqual(gs_exlgst.frobeniusdist(gs_exlgst_verb), 0)
self.assertAlmostEqual(gs_exlgst.frobeniusdist(all_gs_exlgst_tups[-1]),
0)
#Run internal checks on less max-L values (so it doesn't take forever)
gs_exlgst_chk = pygsti.do_iterative_exlgst(ds,
gs_clgst,
self.specs,
self.elgstStrings[0:2],
targetGateset=self.gateset,
svdTruncateTo=4,
verbosity=0,
check_jacobian=True)
gs_exlgst_chk_verb = self.runSilent(pygsti.do_iterative_exlgst,
ds,
gs_clgst,
self.specs,
self.elgstStrings[0:2],
targetGateset=self.gateset,
svdTruncateTo=4,
verbosity=10,
check_jacobian=True)
# RUN BELOW LINES TO SEED SAVED GATESET FILES
#pygsti.io.write_gateset(gs_exlgst,compare_files + "/exlgst.gateset", "Saved Extended-LGST (eLGST) Gateset")
#pygsti.io.write_gateset(gs_exlgst_reg,compare_files + "/exlgst_reg.gateset", "Saved Extended-LGST (eLGST) Gateset w/regularization")
gs_exlgst_compare = pygsti.io.load_gateset(compare_files +
"/exlgst.gateset")
gs_exlgst_reg_compare = pygsti.io.load_gateset(compare_files +
"/exlgst_reg.gateset")
gs_exlgst_go = pygsti.optimize_gauge(gs_exlgst,
'target',
targetGateset=gs_exlgst_compare,
spamWeight=1.0)
gs_exlgst_reg_go = pygsti.optimize_gauge(
gs_exlgst_reg,
'target',
targetGateset=gs_exlgst_reg_compare,
spamWeight=1.0)
self.assertAlmostEqual(gs_exlgst_go.frobeniusdist(gs_exlgst_compare),
0,
places=5)
self.assertAlmostEqual(
gs_exlgst_reg_go.frobeniusdist(gs_exlgst_reg_compare), 0, places=5)
