PyGSTi is able to construct polished report documents, which provide high-level summaries as well as detailed analyses of GST results. Reports are intended to be quick and easy way of analyzing a GST estimate, and pyGSTi's report generation functions are specifically designed to interact with its high-level driver functions (see the high-level algorithms tutorial). Currently there is only a single report generation function, pygsti.report.create_general_report
, which takes one or more Results
objects as input and produces an HTML file as output. The HTML format allows the reports to include interactive plots and switches, making it easy to compare different types of analysis or data sets.
PyGSTi's "general" report creates a stand-alone HTML document which cannot run Python. Thus, all the results displayed in the report must be pre-computed (in Python). If you find yourself wanting to fiddle with things and feel that the general report is too static, please consider using a Workspace
object (see following tutorials) within a Jupyter notebook, where you can intermix report tables/plots and Python. Internally, create_general_report
is just a canned routine which uses a WorkSpace
object to generate various tables and plots and then inserts them into a HTML template.
Note to veteran users: PyGSTi has recently transitioned to producing HTML (rather than LaTeX/PDF) reports. The way to generate such report is largely unchanged, with one important exception. Previously, the Results
object had various report-generation methods included within it. We've found this is too restrictive, as we'd sometimes like to generate a report which utilizes the results from multiple runs of GST (to compare them, for instance). Thus, the Results
class is now just a container for a DataSet
and its related GateSet
s, GatestringStructure
s, etc. All of the report-generation capability is now housed in within separate report functions, which we now demonstrate.
Results
¶We start by performing GST using do_long_sequence_gst
, as usual, to create a Results
object (we could also have just loaded one from file).
import pygsti
from pygsti.construction import std1Q_XYI
gs_target = std1Q_XYI.gs_target
fiducials = std1Q_XYI.fiducials
germs = std1Q_XYI.germs
maxLengths = [1,2,4,8,16]
ds = pygsti.io.load_dataset("tutorial_files/Example_Dataset.txt", cache=True)
#Run GST
gs_target.set_all_parameterizations("TP") #TP-constrained
results = pygsti.do_long_sequence_gst(ds, gs_target, fiducials, fiducials, germs,
maxLengths, verbosity=3)
Loading from cache file: tutorial_files/Example_Dataset.txt.cache --- Gate Sequence Creation --- 1282 sequences created Dataset has 3382 entries: 1282 utilized, 0 requested sequences were missing --- LGST --- Singular values of I_tilde (truncating to first 4 of 6) = 4.243730350963286 1.1796261581655645 0.9627515645786063 0.9424890722054706 0.033826151547621315 0.01692336936843073 Singular values of target I_tilde (truncating to first 4 of 6) = 4.242640687119286 1.414213562373096 1.4142135623730956 1.4142135623730954 2.5038933168948026e-16 2.023452063009528e-16 Resulting gate set: rho0 = TPParameterizedSPAMVec with dimension 4 0.71-0.02 0.03 0.75 Mdefault = TPPOVM with effect vectors: 0: FullyParameterizedSPAMVec with dimension 4 0.73 0 0 0.65 1: ComplementSPAMVec with dimension 4 0.69 0 0-0.65 Gi = TPParameterizedGate with shape (4, 4) 1.00 0 0 0 0.01 0.92-0.03 0.02 0.01-0.01 0.90 0.02 -0.01 0 0 0.91 Gx = TPParameterizedGate with shape (4, 4) 1.00 0 0 0 0 0.91-0.01 0 -0.02-0.02-0.04-0.99 -0.05 0.03 0.81 0 Gy = TPParameterizedGate with shape (4, 4) 1.00 0 0 0 0.05 0 0 0.98 0.01 0 0.89-0.03 -0.06-0.82 0 0 --- Iterative MLGST: Iter 1 of 5 92 gate strings ---: --- Minimum Chi^2 GST --- Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing) groups of ~1 procs each, to distribute over 43 params (taken as 1 param groups of ~43 params). --- Outer Iter 0: norm_f = 86.3537, mu=0, |J|=1010.99 --- Outer Iter 1: norm_f = 49.6491, mu=79.0766, |J|=1009.86 --- Outer Iter 2: norm_f = 49.5669, mu=26.3589, |J|=1008.85 --- Outer Iter 3: norm_f = 49.5665, mu=8.78629, |J|=1008.87 Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06 Sum of Chi^2 = 49.5665 (91 data params - 31 model params = expected mean of 60; p-value = 0.829503) Completed in 0.1s 2*Delta(log(L)) = 49.6936 Iteration 1 took 0.1s --- Iterative MLGST: Iter 2 of 5 168 gate strings ---: --- Minimum Chi^2 GST --- Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing) groups of ~1 procs each, to distribute over 43 params (taken as 1 param groups of ~43 params). --- Outer Iter 0: norm_f = 150.19, mu=0, |J|=1397.23 --- Outer Iter 1: norm_f = 111.389, mu=138.539, |J|=1388.05 --- Outer Iter 2: norm_f = 111.209, mu=46.1798, |J|=1387.46 --- Outer Iter 3: norm_f = 111.208, mu=15.3933, |J|=1387.45 Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06 Sum of Chi^2 = 111.208 (167 data params - 31 model params = expected mean of 136; p-value = 0.941163) Completed in 0.1s 2*Delta(log(L)) = 111.486 Iteration 2 took 0.2s --- Iterative MLGST: Iter 3 of 5 450 gate strings ---: --- Minimum Chi^2 GST --- Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing) groups of ~1 procs each, to distribute over 43 params (taken as 1 param groups of ~43 params). --- Outer Iter 0: norm_f = 498.77, mu=0, |J|=2295.79 --- Outer Iter 1: norm_f = 421.84, mu=346.423, |J|=2300.79 --- Outer Iter 2: norm_f = 421.713, mu=115.474, |J|=2300.65 Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06 Sum of Chi^2 = 421.713 (449 data params - 31 model params = expected mean of 418; p-value = 0.439961) Completed in 0.3s 2*Delta(log(L)) = 422.191 Iteration 3 took 0.3s --- Iterative MLGST: Iter 4 of 5 862 gate strings ---: --- Minimum Chi^2 GST --- Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing) groups of ~1 procs each, to distribute over 43 params (taken as 1 param groups of ~43 params). --- Outer Iter 0: norm_f = 851.493, mu=0, |J|=3309.82 --- Outer Iter 1: norm_f = 806.348, mu=636.017, |J|=3286.21 --- Outer Iter 2: norm_f = 806.308, mu=212.006, |J|=3286.08 Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06 Sum of Chi^2 = 806.308 (861 data params - 31 model params = expected mean of 830; p-value = 0.71591) Completed in 0.5s 2*Delta(log(L)) = 807.505 Iteration 4 took 0.5s --- Iterative MLGST: Iter 5 of 5 1282 gate strings ---: --- Minimum Chi^2 GST --- Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing) groups of ~1 procs each, to distribute over 43 params (taken as 1 param groups of ~43 params). --- Outer Iter 0: norm_f = 1263, mu=0, |J|=4223.66 --- Outer Iter 1: norm_f = 1245.9, mu=917.211, |J|=4227.36 --- Outer Iter 2: norm_f = 1245.88, mu=305.737, |J|=4228.06 Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06 Sum of Chi^2 = 1245.88 (1281 data params - 31 model params = expected mean of 1250; p-value = 0.527561) Completed in 0.7s 2*Delta(log(L)) = 1247.4 Iteration 5 took 0.7s Switching to ML objective (last iteration) --- MLGST --- --- Outer Iter 0: norm_f = 623.698, mu=0, |J|=2989.23 --- Outer Iter 1: norm_f = 623.667, mu=458.353, |J|=2990.87 Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06 Maximum log(L) = 623.667 below upper bound of -2.13594e+06 2*Delta(log(L)) = 1247.33 (1281 data params - 31 model params = expected mean of 1250; p-value = 0.515963) Completed in 0.3s 2*Delta(log(L)) = 1247.33 Final MLGST took 0.3s Iterative MLGST Total Time: 2.1s -- Adding Gauge Optimized (go0) --
Now that we have results
, we use the create_standard_report
method within pygsti.report.factory
to generate a report. If the given filename ends in ".pdf
" then a PDF-format report is generated; otherwise the file name specifies a folder that will be filled with HTML pages. To open a HTML-format report, you open the main.html
file directly inside the report's folder. Setting auto_open=True
makes the finished report open in your web browser automatically.
#HTML
pygsti.report.create_standard_report(results, "tutorial_files/exampleReport",
title="GST Example Report", verbosity=1, auto_open=True)
print("\n")
#PDF
pygsti.report.create_standard_report(results, "tutorial_files/exampleReport.pdf",
title="GST Example Report", verbosity=1, auto_open=True)
*** Creating workspace *** *** Generating switchboard *** Found standard clifford compilation from std1Q_XYI *** Generating tables *** *** Generating plots *** *** Merging into template file *** Output written to tutorial_files/exampleReport directory Opening tutorial_files/exampleReport/main.html... *** Report Generation Complete! Total time 22.514s *** *** Creating workspace *** *** Generating switchboard *** Found standard clifford compilation from std1Q_XYI *** Generating tables *** *** Generating plots *** *** Merging into template file ***
/usr/local/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:424: MatplotlibDeprecationWarning: Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.
Latex file(s) successfully generated. Attempting to compile with pdflatex... Initial output PDF tutorial_files/exampleReport.pdf successfully generated. Final output PDF tutorial_files/exampleReport.pdf successfully generated. Cleaning up .aux and .log files. Opening tutorial_files/exampleReport.pdf... *** Report Generation Complete! Total time 68.6444s ***
<pygsti.report.workspace.Workspace at 0x122f77f60>
There are several remarks about these reports worth noting:
Results
objects that have multiple estimates and/or gauge optimizations, consider using the Results
object's view
method to single out the estimate and gauge optimization you're after.pdflatex
on your system to compile PDF reports.Next, let's analyze the same data two different ways: with and without the TP-constraint (i.e. whether the gates must be trace-preserving) and furthermore gauge optmimize each case using several different SPAM-weights. In each case we'll call do_long_sequence_gst
with gaugeOptParams=False
, so that no gauge optimization is done, and then perform several gauge optimizations separately and add these to the Results
object via its add_gaugeoptimized
function.
#Case1: TP-constrained GST
tpTarget = gs_target.copy()
tpTarget.set_all_parameterizations("TP")
results_tp = pygsti.do_long_sequence_gst(ds, tpTarget, fiducials, fiducials, germs,
maxLengths, gaugeOptParams=False, verbosity=1)
#Gauge optimize
est = results_tp.estimates['default']
gsFinal = est.gatesets['final iteration estimate']
gsTarget = est.gatesets['target']
for spamWt in [1e-4,1e-2,1.0]:
gs = pygsti.gaugeopt_to_target(gsFinal,gsTarget,{'gates':1, 'spam':spamWt})
est.add_gaugeoptimized({'itemWeights': {'gates':1, 'spam':spamWt}}, gs, "Spam %g" % spamWt)
--- Gate Sequence Creation --- --- LGST --- --- Iterative MLGST: [##################################################] 100.0% 1282 gate strings --- Iterative MLGST Total Time: 2.2s
#Case2: "Full" GST
fullTarget = gs_target.copy()
fullTarget.set_all_parameterizations("full")
results_full = pygsti.do_long_sequence_gst(ds, fullTarget, fiducials, fiducials, germs,
maxLengths, gaugeOptParams=False, verbosity=1)
#Gauge optimize
est = results_full.estimates['default']
gsFinal = est.gatesets['final iteration estimate']
gsTarget = est.gatesets['target']
for spamWt in [1e-4,1e-2,1.0]:
gs = pygsti.gaugeopt_to_target(gsFinal,gsTarget,{'gates':1, 'spam':spamWt})
est.add_gaugeoptimized({'itemWeights': {'gates':1, 'spam':spamWt}}, gs, "Spam %g" % spamWt)
--- Gate Sequence Creation --- --- LGST --- --- Iterative MLGST: [##################################################] 100.0% 1282 gate strings --- Iterative MLGST Total Time: 2.6s
We'll now call the same create_standard_report
function but this time instead of passing a single Results
object as the first argument we'll pass a dictionary of them. This will result in a HTML report that includes switches to select which case ("TP" or "Full") as well as which gauge optimization to display output quantities for. PDF reports cannot support this interactivity, and so if you try to generate a PDF report you'll get an error.
ws = pygsti.report.create_standard_report({'TP': results_tp, "Full": results_full},
"tutorial_files/exampleMultiEstimateReport",
title="Example Multi-Estimate Report",
verbosity=2, auto_open=True)
*** Creating workspace *** *** Generating switchboard *** Found standard clifford compilation from std1Q_XYI Found standard clifford compilation from std1Q_XYI *** Generating tables *** targetSpamBriefTable took 0.119606 seconds targetGatesBoxTable took 0.235001 seconds datasetOverviewTable took 0.715112 seconds bestGatesetSpamParametersTable took 0.001678 seconds bestGatesetSpamBriefTable took 1.039131 seconds bestGatesetSpamVsTargetTable took 0.380928 seconds bestGatesetGaugeOptParamsTable took 0.000448 seconds bestGatesetGatesBoxTable took 0.819847 seconds bestGatesetChoiEvalTable took 1.574216 seconds bestGatesetDecompTable took 1.133145 seconds bestGatesetEvalTable took 0.00636 seconds bestGermsEvalTable took 0.030222 seconds
/Users/enielse/research/pyGSTi/packages/pygsti/extras/rb/theory.py:200: UserWarning: Output may be unreliable because the gateset is not approximately trace-preserving.
bestGatesetVsTargetTable took 0.273891 seconds bestGatesVsTargetTable_gv took 1.075788 seconds bestGatesVsTargetTable_gvgerms took 0.471683 seconds bestGatesVsTargetTable_gi took 0.025633 seconds bestGatesVsTargetTable_gigerms took 0.053104 seconds bestGatesVsTargetTable_sum took 0.912212 seconds bestGatesetErrGenBoxTable took 3.341863 seconds metadataTable took 0.002637 seconds stdoutBlock took 0.000179 seconds profilerTable took 0.00135 seconds softwareEnvTable took 0.001768 seconds exampleTable took 0.044646 seconds singleMetricTable_gv took 0.965942 seconds singleMetricTable_gi took 0.063963 seconds fiducialListTable took 0.000544 seconds prepStrListTable took 0.000393 seconds effectStrListTable took 0.00041 seconds colorBoxPlotKeyPlot took 0.055421 seconds germList2ColTable took 0.000471 seconds progressTable took 2.544254 seconds *** Generating plots *** gramBarPlot took 0.123211 seconds progressBarPlot took 0.092685 seconds progressBarPlot_sum took 0.000668 seconds finalFitComparePlot took 0.52919 seconds bestEstimateColorBoxPlot took 11.839346 seconds bestEstimateTVDColorBoxPlot took 15.999738 seconds bestEstimateColorScatterPlot took 30.259677 seconds bestEstimateColorHistogram took 25.785535 seconds progressTable_scl took 0.000188 seconds progressBarPlot_scl took 0.000183 seconds bestEstimateColorBoxPlot_scl took 0.000404 seconds bestEstimateColorScatterPlot_scl took 0.000497 seconds bestEstimateColorHistogram_scl took 0.000423 seconds dataScalingColorBoxPlot took 0.000143 seconds Statistical hypothesis tests did NOT find inconsistency between the datasets at 5.00% significance. Statistical hypothesis tests did NOT find inconsistency between the datasets at 5.00% significance. Statistical hypothesis tests did NOT find inconsistency between the datasets at 5.00% significance. Statistical hypothesis tests did NOT find inconsistency between the datasets at 5.00% significance. dsComparisonSummary took 0.262333 seconds dsComparisonHistogram took 0.983263 seconds dsComparisonBoxPlot took 1.046554 seconds *** Merging into template file *** Rendering topSwitchboard took 0.0002 seconds Rendering maxLSwitchboard1 took 0.000237 seconds Rendering targetSpamBriefTable took 0.027927 seconds Rendering targetGatesBoxTable took 0.019769 seconds Rendering datasetOverviewTable took 0.000878 seconds Rendering bestGatesetSpamParametersTable took 0.0117 seconds Rendering bestGatesetSpamBriefTable took 0.145431 seconds Rendering bestGatesetSpamVsTargetTable took 0.019482 seconds Rendering bestGatesetGaugeOptParamsTable took 0.005697 seconds Rendering bestGatesetGatesBoxTable took 0.115942 seconds Rendering bestGatesetChoiEvalTable took 0.089569 seconds Rendering bestGatesetDecompTable took 0.093545 seconds Rendering bestGatesetEvalTable took 0.054918 seconds Rendering bestGermsEvalTable took 0.192758 seconds Rendering bestGatesetVsTargetTable took 0.007304 seconds Rendering bestGatesVsTargetTable_gv took 0.039026 seconds Rendering bestGatesVsTargetTable_gvgerms took 0.080419 seconds Rendering bestGatesVsTargetTable_gi took 0.010964 seconds Rendering bestGatesVsTargetTable_gigerms took 0.012316 seconds Rendering bestGatesVsTargetTable_sum took 0.035031 seconds Rendering bestGatesetErrGenBoxTable took 0.221355 seconds Rendering metadataTable took 0.036869 seconds Rendering stdoutBlock took 0.001803 seconds Rendering profilerTable took 0.004798 seconds Rendering softwareEnvTable took 0.005801 seconds Rendering exampleTable took 0.005409 seconds Rendering metricSwitchboard_gv took 0.000105 seconds Rendering metricSwitchboard_gi took 8.6e-05 seconds Rendering singleMetricTable_gv took 0.069867 seconds Rendering singleMetricTable_gi took 0.051028 seconds Rendering fiducialListTable took 0.008469 seconds Rendering prepStrListTable took 0.006289 seconds Rendering effectStrListTable took 0.005689 seconds Rendering colorBoxPlotKeyPlot took 0.005451 seconds Rendering germList2ColTable took 0.011337 seconds Rendering progressTable took 0.01671 seconds Rendering gramBarPlot took 0.006437 seconds Rendering progressBarPlot took 0.006058 seconds Rendering progressBarPlot_sum took 0.005275 seconds Rendering finalFitComparePlot took 0.004668 seconds Rendering bestEstimateColorBoxPlot took 0.191276 seconds Rendering bestEstimateTVDColorBoxPlot took 0.177158 seconds Rendering bestEstimateColorScatterPlot took 0.213196 seconds Rendering bestEstimateColorHistogram took 0.15256 seconds Rendering progressTable_scl took 0.001327 seconds Rendering progressBarPlot_scl took 0.001394 seconds Rendering bestEstimateColorBoxPlot_scl took 0.001173 seconds Rendering bestEstimateColorScatterPlot_scl took 0.001238 seconds Rendering bestEstimateColorHistogram_scl took 0.000736 seconds Rendering dataScalingColorBoxPlot took 0.001655 seconds Rendering dscmpSwitchboard took 7.9e-05 seconds Rendering dsComparisonSummary took 0.005384 seconds Rendering dsComparisonHistogram took 0.080512 seconds Rendering dsComparisonBoxPlot took 0.09261 seconds Output written to tutorial_files/exampleMultiEstimateReport directory Opening tutorial_files/exampleMultiEstimateReport/main.html... *** Report Generation Complete! Total time 125.401s ***
In the above call we capture the return value in the variable ws
- a Workspace
object. PyGSTi's Workspace
objects function as both a factory for figures and tables as well as a smart cache for computed values. Within create_standard_report
a Workspace
object is created and used to create all the figures in the report. As an intended side effect, each of these figures is cached, along with some of the intermediate results used to create it. As we'll see below, a Workspace
can also be specified as input to create_standard_report
, allowing it to utilize previously cached quantities.
Note to veteran users: Other report formats such as beamer
-class PDF presentation and Powerpoint presentation have been dropped from pyGSTi. These presentation formats were rarely used and moreover we feel that the HTML format is able to provide all of the functionality that was present in these discontinued formats.
Another way: Because both results_tp
and results_full
above used the same dataset and gate sequences, we could have combined them as two estimates in a single Results
object (see the previous tutorial on pyGSTi's Results
object). This can be done by renaming at least one of the "default"
-named estimates in results_tp
or results_full
(below we rename both) and then adding the estimate within results_full
to the estimates already contained in results_tp
:
results_tp.rename_estimate('default','TP')
results_full.rename_estimate('default','Full')
results_both = results_tp.copy() #copy just for neatness
results_both.add_estimates(results_full, estimatesToAdd=['Full'])
Creating a report using results_both
will result in the same report we just generated. We'll demonstrate this anyway, but in addition we'll supply create_standard_report
a ws
argument, which tells it to use any cached values contained in a given input Workspace
to expedite report generation. Since our workspace object has the exact quantities we need cached in it, you'll notice a significant speedup. Finally, note that even though there's just a single Results
object, you still can't generate a PDF report from it because it contains multiple estimates.
pygsti.report.create_standard_report(results_both,
"tutorial_files/exampleMultiEstimateReport2",
title="Example Multi-Estimate Report (v2)",
verbosity=2, auto_open=True, ws=ws)
*** Creating workspace *** *** Generating switchboard *** Found standard clifford compilation from std1Q_XYI Found standard clifford compilation from std1Q_XYI *** Generating tables *** targetSpamBriefTable took 0.001625 seconds targetGatesBoxTable took 0.000545 seconds datasetOverviewTable took 0.000385 seconds bestGatesetSpamParametersTable took 0.002367 seconds bestGatesetSpamBriefTable took 0.001667 seconds bestGatesetSpamVsTargetTable took 0.001447 seconds bestGatesetGaugeOptParamsTable took 0.000703 seconds bestGatesetGatesBoxTable took 0.002244 seconds bestGatesetChoiEvalTable took 0.001901 seconds bestGatesetDecompTable took 0.001297 seconds bestGatesetEvalTable took 0.000608 seconds bestGermsEvalTable took 0.082869 seconds bestGatesetVsTargetTable took 0.016313 seconds bestGatesVsTargetTable_gv took 0.001878 seconds bestGatesVsTargetTable_gvgerms took 0.003001 seconds bestGatesVsTargetTable_gi took 0.000648 seconds bestGatesVsTargetTable_gigerms took 0.082485 seconds bestGatesVsTargetTable_sum took 0.001462 seconds bestGatesetErrGenBoxTable took 0.001707 seconds metadataTable took 0.006909 seconds stdoutBlock took 0.000204 seconds profilerTable took 0.001722 seconds softwareEnvTable took 0.001023 seconds exampleTable took 0.00017 seconds singleMetricTable_gv took 2.300172 seconds singleMetricTable_gi took 0.096024 seconds fiducialListTable took 0.000355 seconds prepStrListTable took 0.000292 seconds effectStrListTable took 0.000262 seconds colorBoxPlotKeyPlot took 0.000626 seconds germList2ColTable took 0.000487 seconds progressTable took 1.001779 seconds *** Generating plots *** gramBarPlot took 0.001016 seconds progressBarPlot took 0.099056 seconds progressBarPlot_sum took 0.001599 seconds finalFitComparePlot took 0.087454 seconds bestEstimateColorBoxPlot took 10.291937 seconds bestEstimateTVDColorBoxPlot took 9.786226 seconds bestEstimateColorScatterPlot took 13.687434 seconds bestEstimateColorHistogram took 10.345748 seconds progressTable_scl took 0.000144 seconds progressBarPlot_scl took 0.000232 seconds bestEstimateColorBoxPlot_scl took 0.000397 seconds bestEstimateColorScatterPlot_scl took 0.000389 seconds bestEstimateColorHistogram_scl took 0.000407 seconds dataScalingColorBoxPlot took 0.000129 seconds *** Merging into template file *** Rendering topSwitchboard took 0.000211 seconds Rendering maxLSwitchboard1 took 0.000177 seconds Rendering targetSpamBriefTable took 0.038761 seconds Rendering targetGatesBoxTable took 0.01991 seconds Rendering datasetOverviewTable took 0.00157 seconds Rendering bestGatesetSpamParametersTable took 0.011264 seconds Rendering bestGatesetSpamBriefTable took 0.149068 seconds Rendering bestGatesetSpamVsTargetTable took 0.018368 seconds Rendering bestGatesetGaugeOptParamsTable took 0.005744 seconds Rendering bestGatesetGatesBoxTable took 0.116312 seconds Rendering bestGatesetChoiEvalTable took 0.093703 seconds Rendering bestGatesetDecompTable took 0.093833 seconds Rendering bestGatesetEvalTable took 0.053966 seconds Rendering bestGermsEvalTable took 0.19973 seconds Rendering bestGatesetVsTargetTable took 0.00741 seconds Rendering bestGatesVsTargetTable_gv took 0.038853 seconds Rendering bestGatesVsTargetTable_gvgerms took 0.084407 seconds Rendering bestGatesVsTargetTable_gi took 0.011099 seconds Rendering bestGatesVsTargetTable_gigerms took 0.011745 seconds Rendering bestGatesVsTargetTable_sum took 0.034155 seconds Rendering bestGatesetErrGenBoxTable took 0.217628 seconds Rendering metadataTable took 0.03614 seconds Rendering stdoutBlock took 0.001801 seconds Rendering profilerTable took 0.005329 seconds Rendering softwareEnvTable took 0.005644 seconds Rendering exampleTable took 0.004483 seconds Rendering metricSwitchboard_gv took 6.8e-05 seconds Rendering metricSwitchboard_gi took 6.4e-05 seconds Rendering singleMetricTable_gv took 0.046311 seconds Rendering singleMetricTable_gi took 0.031409 seconds Rendering fiducialListTable took 0.004477 seconds Rendering prepStrListTable took 0.004269 seconds Rendering effectStrListTable took 0.003441 seconds Rendering colorBoxPlotKeyPlot took 0.005092 seconds Rendering germList2ColTable took 0.025014 seconds Rendering progressTable took 0.019318 seconds Rendering gramBarPlot took 0.011102 seconds Rendering progressBarPlot took 0.009698 seconds Rendering progressBarPlot_sum took 0.005631 seconds Rendering finalFitComparePlot took 0.004943 seconds Rendering bestEstimateColorBoxPlot took 0.188679 seconds Rendering bestEstimateTVDColorBoxPlot took 0.170919 seconds Rendering bestEstimateColorScatterPlot took 0.214592 seconds Rendering bestEstimateColorHistogram took 0.133369 seconds Rendering progressTable_scl took 0.001591 seconds Rendering progressBarPlot_scl took 0.0015 seconds Rendering bestEstimateColorBoxPlot_scl took 0.001322 seconds Rendering bestEstimateColorScatterPlot_scl took 0.001314 seconds Rendering bestEstimateColorHistogram_scl took 0.001272 seconds Rendering dataScalingColorBoxPlot took 0.000717 seconds Output written to tutorial_files/exampleMultiEstimateReport2 directory Opening tutorial_files/exampleMultiEstimateReport2/main.html... *** Report Generation Complete! Total time 50.5728s ***
<pygsti.report.workspace.Workspace at 0x129656978>
do_stdpractice_gst
¶It's no coincidence that a Results
object containing multiple estimates using the same data is precisely what's returned from do_stdpractice_gst
(see docstring for information on its arguments). This allows one to run GST multiple times, creating several different "standard" estimates and gauge optimizations, and plot them all in a single (HTML) report.
results_std = pygsti.do_stdpractice_gst(ds, gs_target, fiducials, fiducials, germs,
maxLengths, verbosity=4, modes="TP,CPTP,Target",
gaugeOptSuite=('single','toggleValidSpam'))
# Generate a report with "TP", "CPTP", and "Target" estimates
pygsti.report.create_standard_report(results_std, "tutorial_files/exampleStdReport",
title="Post StdPractice Report", auto_open=True,
verbosity=1)
-- Std Practice: Iter 1 of 3 (TP) --: --- Gate Sequence Creation --- 1282 sequences created Dataset has 3382 entries: 1282 utilized, 0 requested sequences were missing --- LGST --- Singular values of I_tilde (truncating to first 4 of 6) = 4.243730350963286 1.1796261581655645 0.9627515645786063 0.9424890722054706 0.033826151547621315 0.01692336936843073 Singular values of target I_tilde (truncating to first 4 of 6) = 4.242640687119286 1.414213562373096 1.4142135623730956 1.4142135623730954 2.5038933168948026e-16 2.023452063009528e-16 Resulting gate set: rho0 = TPParameterizedSPAMVec with dimension 4 0.71-0.02 0.03 0.75 Mdefault = TPPOVM with effect vectors: 0: FullyParameterizedSPAMVec with dimension 4 0.73 0 0 0.65 1: ComplementSPAMVec with dimension 4 0.69 0 0-0.65 Gi = TPParameterizedGate with shape (4, 4) 1.00 0 0 0 0.01 0.92-0.03 0.02 0.01-0.01 0.90 0.02 -0.01 0 0 0.91 Gx = TPParameterizedGate with shape (4, 4) 1.00 0 0 0 0 0.91-0.01 0 -0.02-0.02-0.04-0.99 -0.05 0.03 0.81 0 Gy = TPParameterizedGate with shape (4, 4) 1.00 0 0 0 0.05 0 0 0.98 0.01 0 0.89-0.03 -0.06-0.82 0 0 --- Iterative MLGST: Iter 1 of 5 92 gate strings ---: --- Minimum Chi^2 GST --- Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing) groups of ~1 procs each, to distribute over 43 params (taken as 1 param groups of ~43 params). --- Outer Iter 0: norm_f = 86.3537, mu=0, |J|=1010.99 --- Outer Iter 1: norm_f = 49.6491, mu=79.0766, |J|=1009.86 --- Outer Iter 2: norm_f = 49.5669, mu=26.3589, |J|=1008.85 --- Outer Iter 3: norm_f = 49.5665, mu=8.78629, |J|=1008.87 Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06 Sum of Chi^2 = 49.5665 (91 data params - 31 model params = expected mean of 60; p-value = 0.829503) Completed in 0.3s 2*Delta(log(L)) = 49.6936 Iteration 1 took 0.3s --- Iterative MLGST: Iter 2 of 5 168 gate strings ---: --- Minimum Chi^2 GST --- Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing) groups of ~1 procs each, to distribute over 43 params (taken as 1 param groups of ~43 params). --- Outer Iter 0: norm_f = 150.19, mu=0, |J|=1397.23 --- Outer Iter 1: norm_f = 111.389, mu=138.539, |J|=1388.05 --- Outer Iter 2: norm_f = 111.209, mu=46.1798, |J|=1387.46 --- Outer Iter 3: norm_f = 111.208, mu=15.3933, |J|=1387.45 Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06 Sum of Chi^2 = 111.208 (167 data params - 31 model params = expected mean of 136; p-value = 0.941163) Completed in 0.3s 2*Delta(log(L)) = 111.486 Iteration 2 took 0.3s --- Iterative MLGST: Iter 3 of 5 450 gate strings ---: --- Minimum Chi^2 GST --- Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing) groups of ~1 procs each, to distribute over 43 params (taken as 1 param groups of ~43 params). --- Outer Iter 0: norm_f = 498.77, mu=0, |J|=2295.79 --- Outer Iter 1: norm_f = 421.84, mu=346.423, |J|=2300.79 --- Outer Iter 2: norm_f = 421.713, mu=115.474, |J|=2300.65 Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06 Sum of Chi^2 = 421.713 (449 data params - 31 model params = expected mean of 418; p-value = 0.439961) Completed in 0.6s 2*Delta(log(L)) = 422.191 Iteration 3 took 0.6s --- Iterative MLGST: Iter 4 of 5 862 gate strings ---: --- Minimum Chi^2 GST --- Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing) groups of ~1 procs each, to distribute over 43 params (taken as 1 param groups of ~43 params). --- Outer Iter 0: norm_f = 851.493, mu=0, |J|=3309.82 --- Outer Iter 1: norm_f = 806.348, mu=636.017, |J|=3286.21 --- Outer Iter 2: norm_f = 806.308, mu=212.006, |J|=3286.08 Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06 Sum of Chi^2 = 806.308 (861 data params - 31 model params = expected mean of 830; p-value = 0.71591) Completed in 1.1s 2*Delta(log(L)) = 807.505 Iteration 4 took 1.1s --- Iterative MLGST: Iter 5 of 5 1282 gate strings ---: --- Minimum Chi^2 GST --- Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing) groups of ~1 procs each, to distribute over 43 params (taken as 1 param groups of ~43 params). --- Outer Iter 0: norm_f = 1263, mu=0, |J|=4223.66 --- Outer Iter 1: norm_f = 1245.9, mu=917.211, |J|=4227.36 --- Outer Iter 2: norm_f = 1245.88, mu=305.737, |J|=4228.06 Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06 Sum of Chi^2 = 1245.88 (1281 data params - 31 model params = expected mean of 1250; p-value = 0.527561) Completed in 1.6s 2*Delta(log(L)) = 1247.4 Iteration 5 took 1.7s Switching to ML objective (last iteration) --- MLGST --- --- Outer Iter 0: norm_f = 623.698, mu=0, |J|=2989.23 --- Outer Iter 1: norm_f = 623.667, mu=458.353, |J|=2990.87 Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06 Maximum log(L) = 623.667 below upper bound of -2.13594e+06 2*Delta(log(L)) = 1247.33 (1281 data params - 31 model params = expected mean of 1250; p-value = 0.515963) Completed in 0.8s 2*Delta(log(L)) = 1247.33 Final MLGST took 0.8s Iterative MLGST Total Time: 4.9s -- Performing 'single' gauge optimization on TP estimate -- -- Adding Gauge Optimized (single) -- -- Performing 'Spam 0.001' gauge optimization on TP estimate -- -- Adding Gauge Optimized (Spam 0.001) -- -- Performing 'Spam 0.001+v' gauge optimization on TP estimate -- -- Adding Gauge Optimized (Spam 0.001+v) -- -- Std Practice: Iter 2 of 3 (CPTP) --: --- Gate Sequence Creation --- 1282 sequences created Dataset has 3382 entries: 1282 utilized, 0 requested sequences were missing --- Iterative MLGST: Iter 1 of 5 92 gate strings ---: --- Minimum Chi^2 GST --- Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing) groups of ~1 procs each, to distribute over 60 params (taken as 1 param groups of ~60 params). --- Outer Iter 0: norm_f = 1.10824e+07, mu=0, |J|=1098.32 --- Outer Iter 1: norm_f = 525198, mu=152.044, |J|=23245 --- Outer Iter 2: norm_f = 105604, mu=119.002, |J|=4968.62 --- Outer Iter 3: norm_f = 17775.9, mu=81.851, |J|=1539.41 --- Outer Iter 4: norm_f = 2118.67, mu=39.4495, |J|=988.314 --- Outer Iter 5: norm_f = 91.4772, mu=13.1498, |J|=781.246 --- Outer Iter 6: norm_f = 66.6366, mu=10.0856, |J|=746.609 --- Outer Iter 7: norm_f = 59.8988, mu=16.5022, |J|=744.779 --- Outer Iter 8: norm_f = 55.2767, mu=32.1916, |J|=740.96 --- Outer Iter 9: norm_f = 50.7549, mu=24.8843, |J|=744.012 --- Outer Iter 10: norm_f = 49.7522, mu=8.29476, |J|=747.925 --- Outer Iter 11: norm_f = 49.7405, mu=48.5801, |J|=748.36 --- Outer Iter 12: norm_f = 49.7394, mu=44.6982, |J|=748.432 --- Outer Iter 13: norm_f = 49.739, mu=34.6233, |J|=748.465 --- Outer Iter 14: norm_f = 49.7386, mu=29.4361, |J|=748.5 --- Outer Iter 15: norm_f = 49.7383, mu=29.4353, |J|=748.54 --- Outer Iter 16: norm_f = 49.7383, mu=46.4292, |J|=748.574 --- Outer Iter 17: norm_f = 49.7379, mu=53.6274, |J|=748.601 --- Outer Iter 18: norm_f = 49.7376, mu=54.0897, |J|=748.637 --- Outer Iter 19: norm_f = 49.7374, mu=53.7448, |J|=748.669 --- Outer Iter 20: norm_f = 49.7372, mu=41.3964, |J|=748.695 --- Outer Iter 21: norm_f = 49.737, mu=19.6625, |J|=748.727 --- Outer Iter 22: norm_f = 49.7367, mu=15.9398, |J|=748.787 --- Outer Iter 23: norm_f = 49.7365, mu=33.1175, |J|=748.829 --- Outer Iter 24: norm_f = 49.7365, mu=52.8695, |J|=748.861 --- Outer Iter 25: norm_f = 49.7362, mu=54.9158, |J|=748.889 --- Outer Iter 26: norm_f = 49.736, mu=54.9155, |J|=748.92 --- Outer Iter 27: norm_f = 49.7359, mu=48.065, |J|=748.945 --- Outer Iter 28: norm_f = 49.7357, mu=23.5141, |J|=748.971 --- Outer Iter 29: norm_f = 49.7355, mu=13.3491, |J|=749.021 --- Outer Iter 30: norm_f = 49.7355, mu=22.8901, |J|=749.097 --- Outer Iter 31: norm_f = 49.7352, mu=58.1488, |J|=749.123 --- Outer Iter 32: norm_f = 49.735, mu=58.7562, |J|=749.156 --- Outer Iter 33: norm_f = 49.7349, mu=58.067, |J|=749.182 --- Outer Iter 34: norm_f = 49.7348, mu=32.8342, |J|=749.203 --- Outer Iter 35: norm_f = 49.7347, mu=10.9447, |J|=749.237 --- Outer Iter 36: norm_f = 49.7344, mu=10.5674, |J|=749.327 --- Outer Iter 37: norm_f = 49.7342, mu=82.2964, |J|=749.355 Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06 Sum of Chi^2 = 49.7342 (91 data params - 31 model params = expected mean of 60; p-value = 0.82506) Completed in 2.9s 2*Delta(log(L)) = 49.8652 Iteration 1 took 2.9s --- Iterative MLGST: Iter 2 of 5 168 gate strings ---: --- Minimum Chi^2 GST --- Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing) groups of ~1 procs each, to distribute over 60 params (taken as 1 param groups of ~60 params). --- Outer Iter 0: norm_f = 151.528, mu=0, |J|=1014.34 --- Outer Iter 1: norm_f = 122.487, mu=173.839, |J|=987.258 --- Outer Iter 2: norm_f = 112.801, mu=57.9464, |J|=996.402 --- Outer Iter 3: norm_f = 111.668, mu=31.1358, |J|=999.747 --- Outer Iter 4: norm_f = 111.484, mu=10.3786, |J|=1002.08 --- Outer Iter 5: norm_f = 111.476, mu=4.36121, |J|=1002.23 --- Outer Iter 6: norm_f = 111.475, mu=93.0392, |J|=1002.33 --- Outer Iter 7: norm_f = 111.475, mu=88.9505, |J|=1002.32 --- Outer Iter 8: norm_f = 111.475, mu=58.1524, |J|=1002.3 --- Outer Iter 9: norm_f = 111.474, mu=28.2488, |J|=1002.26 --- Outer Iter 10: norm_f = 111.474, mu=27.91, |J|=1002.16 --- Outer Iter 11: norm_f = 111.474, mu=91.7908, |J|=1002.11 --- Outer Iter 12: norm_f = 111.474, mu=94.8231, |J|=1002.09 --- Outer Iter 13: norm_f = 111.474, mu=94.8121, |J|=1002.07 --- Outer Iter 14: norm_f = 111.474, mu=78.1122, |J|=1002.05 --- Outer Iter 15: norm_f = 111.474, mu=32.9363, |J|=1002.02 --- Outer Iter 16: norm_f = 111.473, mu=20.4164, |J|=1001.95 --- Outer Iter 17: norm_f = 111.473, mu=42.9239, |J|=1001.9 --- Outer Iter 18: norm_f = 111.473, mu=88.1611, |J|=1001.88 --- Outer Iter 19: norm_f = 111.473, mu=88.1588, |J|=1001.86 --- Outer Iter 20: norm_f = 111.473, mu=80.7668, |J|=1001.83 Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06 Sum of Chi^2 = 111.473 (167 data params - 31 model params = expected mean of 136; p-value = 0.93898) Completed in 1.9s 2*Delta(log(L)) = 111.765 Iteration 2 took 1.9s --- Iterative MLGST: Iter 3 of 5 450 gate strings ---: --- Minimum Chi^2 GST --- Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing) groups of ~1 procs each, to distribute over 60 params (taken as 1 param groups of ~60 params). --- Outer Iter 0: norm_f = 496.83, mu=0, |J|=1622.21 --- Outer Iter 1: norm_f = 425.12, mu=172.635, |J|=1614.05 --- Outer Iter 2: norm_f = 422.084, mu=57.545, |J|=1622.3 --- Outer Iter 3: norm_f = 422.023, mu=19.1817, |J|=1623.99 --- Outer Iter 4: norm_f = 422.006, mu=19.1873, |J|=1622.75 --- Outer Iter 5: norm_f = 421.891, mu=19.3636, |J|=1622.89 --- Outer Iter 6: norm_f = 421.713, mu=6.45454, |J|=1625.17 Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06 Sum of Chi^2 = 421.713 (449 data params - 31 model params = expected mean of 418; p-value = 0.439962) Completed in 1.4s 2*Delta(log(L)) = 422.195 Iteration 3 took 1.4s --- Iterative MLGST: Iter 4 of 5 862 gate strings ---: --- Minimum Chi^2 GST --- Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing) groups of ~1 procs each, to distribute over 60 params (taken as 1 param groups of ~60 params). --- Outer Iter 0: norm_f = 851.551, mu=0, |J|=2237.29 --- Outer Iter 1: norm_f = 813.414, mu=295.355, |J|=2217.99 --- Outer Iter 2: norm_f = 811.823, mu=324.855, |J|=2226.8 --- Outer Iter 3: norm_f = 807.477, mu=108.285, |J|=2242.71 --- Outer Iter 4: norm_f = 807.406, mu=110.399, |J|=2245.32 --- Outer Iter 5: norm_f = 807.331, mu=685.515, |J|=2246.43 --- Outer Iter 6: norm_f = 807.319, mu=654.519, |J|=2246.59 --- Outer Iter 7: norm_f = 807.317, mu=520.891, |J|=2246.64 --- Outer Iter 8: norm_f = 807.316, mu=173.63, |J|=2246.59 --- Outer Iter 9: norm_f = 807.313, mu=57.8768, |J|=2246.3 --- Outer Iter 10: norm_f = 807.305, mu=33.5977, |J|=2245.2 --- Outer Iter 11: norm_f = 807.302, mu=42.4819, |J|=2243.22 --- Outer Iter 12: norm_f = 807.289, mu=339.581, |J|=2243.46 --- Outer Iter 13: norm_f = 807.287, mu=339.35, |J|=2243.35 --- Outer Iter 14: norm_f = 807.286, mu=257.343, |J|=2243.19 --- Outer Iter 15: norm_f = 807.284, mu=85.7811, |J|=2242.94 --- Outer Iter 16: norm_f = 807.279, mu=59.0317, |J|=2242.14 --- Outer Iter 17: norm_f = 807.277, mu=416.797, |J|=2242.06 --- Outer Iter 18: norm_f = 807.276, mu=138.932, |J|=2241.91 --- Outer Iter 19: norm_f = 807.273, mu=46.3107, |J|=2241.4 --- Outer Iter 20: norm_f = 807.264, mu=28.4189, |J|=2239.71 --- Outer Iter 21: norm_f = 807.259, mu=31.8977, |J|=2236.67 --- Outer Iter 22: norm_f = 807.244, mu=204.052, |J|=2236.95 --- Outer Iter 23: norm_f = 807.242, mu=218.317, |J|=2236.51 --- Outer Iter 24: norm_f = 807.24, mu=261.98, |J|=2236.1 --- Outer Iter 25: norm_f = 807.237, mu=280.135, |J|=2235.78 --- Outer Iter 26: norm_f = 807.235, mu=281.486, |J|=2235.49 --- Outer Iter 27: norm_f = 807.232, mu=280.84, |J|=2235.19 --- Outer Iter 28: norm_f = 807.23, mu=256.204, |J|=2234.88 --- Outer Iter 29: norm_f = 807.228, mu=183.617, |J|=2234.53 --- Outer Iter 30: norm_f = 807.225, mu=136.089, |J|=2234.03 --- Outer Iter 31: norm_f = 807.222, mu=136.089, |J|=2233.32 --- Outer Iter 32: norm_f = 807.22, mu=274.362, |J|=2233 --- Outer Iter 33: norm_f = 807.217, mu=274.291, |J|=2232.68 --- Outer Iter 34: norm_f = 807.215, mu=262.392, |J|=2232.36 --- Outer Iter 35: norm_f = 807.213, mu=211.362, |J|=2232.01 --- Outer Iter 36: norm_f = 807.21, mu=155.216, |J|=2231.56 --- Outer Iter 37: norm_f = 807.207, mu=149.698, |J|=2230.95 --- Outer Iter 38: norm_f = 807.206, mu=194.214, |J|=2230.29 --- Outer Iter 39: norm_f = 807.201, mu=388.426, |J|=2230.1 --- Outer Iter 40: norm_f = 807.2, mu=182.505, |J|=2229.9 --- Outer Iter 41: norm_f = 807.197, mu=60.835, |J|=2229.38 --- Outer Iter 42: norm_f = 807.19, mu=52.2799, |J|=2227.73 --- Outer Iter 43: norm_f = 807.189, mu=154.658, |J|=2226.96 --- Outer Iter 44: norm_f = 807.183, mu=320.477, |J|=2226.72 --- Outer Iter 45: norm_f = 807.181, mu=320.459, |J|=2226.53 --- Outer Iter 46: norm_f = 807.179, mu=263.467, |J|=2226.29 --- Outer Iter 47: norm_f = 807.177, mu=98.9462, |J|=2225.97 --- Outer Iter 48: norm_f = 807.173, mu=65.2178, |J|=2225.1 --- Outer Iter 49: norm_f = 807.171, mu=138.167, |J|=2224.51 --- Outer Iter 50: norm_f = 807.169, mu=286.444, |J|=2224.25 --- Outer Iter 51: norm_f = 807.167, mu=286.45, |J|=2224.02 --- Outer Iter 52: norm_f = 807.165, mu=273.194, |J|=2223.78 --- Outer Iter 53: norm_f = 807.164, mu=192.275, |J|=2223.52 --- Outer Iter 54: norm_f = 807.162, mu=113.874, |J|=2223.15 --- Outer Iter 55: norm_f = 807.159, mu=112.429, |J|=2222.53 --- Outer Iter 56: norm_f = 807.158, mu=234.493, |J|=2222.26 --- Outer Iter 57: norm_f = 807.157, mu=243.493, |J|=2221.99 --- Outer Iter 58: norm_f = 807.155, mu=247.11, |J|=2221.75 --- Outer Iter 59: norm_f = 807.154, mu=247.511, |J|=2221.51 --- Outer Iter 60: norm_f = 807.153, mu=247.504, |J|=2221.29 --- Outer Iter 61: norm_f = 807.151, mu=246.07, |J|=2221.06 --- Outer Iter 62: norm_f = 807.15, mu=237.941, |J|=2220.84 --- Outer Iter 63: norm_f = 807.149, mu=219.598, |J|=2220.62 --- Outer Iter 64: norm_f = 807.148, mu=200.308, |J|=2220.39 --- Outer Iter 65: norm_f = 807.147, mu=193.826, |J|=2220.14 --- Outer Iter 66: norm_f = 807.146, mu=193.82, |J|=2219.89 --- Outer Iter 67: norm_f = 807.145, mu=200.435, |J|=2219.65 --- Outer Iter 68: norm_f = 807.145, mu=243.545, |J|=2219.42 --- Outer Iter 69: norm_f = 807.144, mu=270.619, |J|=2219.25 --- Outer Iter 70: norm_f = 807.143, mu=274.777, |J|=2219.11 --- Outer Iter 71: norm_f = 807.142, mu=274.754, |J|=2218.97 --- Outer Iter 72: norm_f = 807.142, mu=259.713, |J|=2218.83 Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06 Sum of Chi^2 = 807.142 (861 data params - 31 model params = expected mean of 830; p-value = 0.708796) Completed in 18.9s 2*Delta(log(L)) = 808.459 Iteration 4 took 18.9s --- Iterative MLGST: Iter 5 of 5 1282 gate strings ---: --- Minimum Chi^2 GST --- Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing) groups of ~1 procs each, to distribute over 60 params (taken as 1 param groups of ~60 params). --- Outer Iter 0: norm_f = 1264.71, mu=0, |J|=2587.64 --- Outer Iter 1: norm_f = 1247.44, mu=339.199, |J|=2580.66 --- Outer Iter 2: norm_f = 1247.19, mu=113.066, |J|=2583.38 --- Outer Iter 3: norm_f = 1247.18, mu=53.3976, |J|=2582.29 --- Outer Iter 4: norm_f = 1247.17, mu=106.788, |J|=2581.2 --- Outer Iter 5: norm_f = 1247.17, mu=782.976, |J|=2581.24 Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06 Sum of Chi^2 = 1247.17 (1281 data params - 31 model params = expected mean of 1250; p-value = 0.51728) Completed in 3.0s 2*Delta(log(L)) = 1248.89 Iteration 5 took 3.0s Switching to ML objective (last iteration) --- MLGST --- --- Outer Iter 0: norm_f = 624.444, mu=0, |J|=1825.13 --- Outer Iter 1: norm_f = 624.418, mu=169.836, |J|=1826.33 --- Outer Iter 2: norm_f = 624.417, mu=56.6119, |J|=1826.11 --- Outer Iter 3: norm_f = 624.414, mu=42.9571, |J|=1825.44 --- Outer Iter 4: norm_f = 624.413, mu=290.126, |J|=1825.43 Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06 Maximum log(L) = 624.413 below upper bound of -2.13594e+06 2*Delta(log(L)) = 1248.83 (1281 data params - 31 model params = expected mean of 1250; p-value = 0.504048) Completed in 2.2s 2*Delta(log(L)) = 1248.83 Final MLGST took 2.2s Iterative MLGST Total Time: 30.3s -- Performing 'single' gauge optimization on CPTP estimate -- -- Adding Gauge Optimized (single) -- -- Performing 'Spam 0.001' gauge optimization on CPTP estimate -- -- Adding Gauge Optimized (Spam 0.001) -- -- Performing 'Spam 0.001+v' gauge optimization on CPTP estimate -- -- Adding Gauge Optimized (Spam 0.001+v) -- -- Std Practice: Iter 3 of 3 (Target) --: --- Gate Sequence Creation --- 1282 sequences created Dataset has 3382 entries: 1282 utilized, 0 requested sequences were missing -- Performing 'single' gauge optimization on Target estimate -- -- Adding Gauge Optimized (single) -- -- Performing 'Spam 0.001' gauge optimization on Target estimate -- -- Adding Gauge Optimized (Spam 0.001) -- -- Performing 'Spam 0.001+v' gauge optimization on Target estimate -- -- Adding Gauge Optimized (Spam 0.001+v) -- *** Creating workspace *** *** Generating switchboard *** Found standard clifford compilation from std1Q_XYI Found standard clifford compilation from std1Q_XYI Found standard clifford compilation from std1Q_XYI *** Generating tables *** *** Generating plots *** *** Merging into template file *** Output written to tutorial_files/exampleStdReport directory Opening tutorial_files/exampleStdReport/main.html... *** Report Generation Complete! Total time 151.024s ***
<pygsti.report.workspace.Workspace at 0x12adb7080>
To display confidence intervals for reported quantities, you must do two things:
confidenceLevel
argument to create_standard_report
.Constructing a factory often means computing a Hessian, which can be time consuming, and so this is not done automatically. Here we demonstrate how to construct a valid factory for the "Spam 0.001" gauge-optimization of the "CPTP" estimate by computing and then projecting the Hessian of the likelihood function.
#Construct and initialize a "confidence region factory" for the CPTP estimate
crfact = results_std.estimates["CPTP"].add_confidence_region_factory('Spam 0.001', 'final')
crfact.compute_hessian(comm=None) #we could use more processors
crfact.project_hessian('intrinsic error')
pygsti.report.create_standard_report(results_std, "tutorial_files/exampleStdReport2",
title="Post StdPractice Report (w/CIs on CPTP)",
confidenceLevel=95, auto_open=True, verbosity=1)
--- Hessian Projector Optimization from separate SPAM and Gate weighting --- Resulting intrinsic errors: 0.00764703 (gates), 0.00530883 (spam) Resulting sqrt(mean(gateCIs**2)): 0.0156693 Resulting sqrt(mean(spamCIs**2)): 0.0132339 *** Creating workspace *** *** Generating switchboard *** Found standard clifford compilation from std1Q_XYI Found standard clifford compilation from std1Q_XYI Found standard clifford compilation from std1Q_XYI *** Generating tables *** *** Generating plots *** *** Merging into template file *** Output written to tutorial_files/exampleStdReport2 directory Opening tutorial_files/exampleStdReport2/main.html... *** Report Generation Complete! Total time 152.495s ***
<pygsti.report.workspace.Workspace at 0x13c9416d8>
We've already seen above that create_standard_report
can be given a dictionary of Results
objects instead of a single one. This allows the creation of reports containing estimates for different DataSet
s (each Results
object only holds estimates for a single DataSet
). Furthermore, when the data sets have the same gate sequences, they will be compared within a tab of the HTML report.
Below, we generate a new data set with the same sequences as the one loaded at the beginning of this tutorial, proceed to run standard-practice GST on that dataset, and create a report of the results along with those of the original dataset. Look at the "Data Comparison" tab within the gauge-invariant error metrics category.
#Make another dataset & estimates
depol_gateset = gs_target.depolarize(gate_noise=0.1)
datagen_gateset = depol_gateset.rotate((0.05,0,0.03))
#Compute the sequences needed to perform Long Sequence GST on
# this GateSet with sequences up to lenth 512
gatestring_list = pygsti.construction.make_lsgst_experiment_list(
std1Q_XYI.gs_target, std1Q_XYI.prepStrs, std1Q_XYI.effectStrs,
std1Q_XYI.germs, [1,2,4,8,16,32,64,128,256,512])
ds2 = pygsti.construction.generate_fake_data(datagen_gateset, gatestring_list, nSamples=1000,
sampleError='binomial', seed=2018)
results_std2 = pygsti.do_stdpractice_gst(ds2, gs_target, fiducials, fiducials, germs,
maxLengths, verbosity=3, modes="TP,CPTP,Target",
gaugeOptSuite=('single','toggleValidSpam'))
pygsti.report.create_standard_report({'DS1': results_std, 'DS2': results_std2},
"tutorial_files/exampleMultiDataSetReport",
title="Example Multi-Dataset Report",
auto_open=True, verbosity=1)
-- Std Practice: Iter 1 of 3 (TP) --: --- Gate Sequence Creation --- 1282 sequences created Dataset has 3382 entries: 1282 utilized, 0 requested sequences were missing --- LGST --- Singular values of I_tilde (truncating to first 4 of 6) = 4.244829997162508 1.1936677889884049 0.9868539533169907 0.932197724091589 0.04714742318656945 0.012700520808584604 Singular values of target I_tilde (truncating to first 4 of 6) = 4.242640687119286 1.414213562373096 1.4142135623730956 1.4142135623730954 2.5038933168948026e-16 2.023452063009528e-16 --- Iterative MLGST: Iter 1 of 5 92 gate strings ---: --- Minimum Chi^2 GST --- Sum of Chi^2 = 47.848 (91 data params - 31 model params = expected mean of 60; p-value = 0.871295) Completed in 0.2s 2*Delta(log(L)) = 47.897 Iteration 1 took 0.2s --- Iterative MLGST: Iter 2 of 5 168 gate strings ---: --- Minimum Chi^2 GST --- Sum of Chi^2 = 112.296 (167 data params - 31 model params = expected mean of 136; p-value = 0.931805) Completed in 0.2s 2*Delta(log(L)) = 112.295 Iteration 2 took 0.2s --- Iterative MLGST: Iter 3 of 5 450 gate strings ---: --- Minimum Chi^2 GST --- Sum of Chi^2 = 409.638 (449 data params - 31 model params = expected mean of 418; p-value = 0.605678) Completed in 0.6s 2*Delta(log(L)) = 409.806 Iteration 3 took 0.6s --- Iterative MLGST: Iter 4 of 5 862 gate strings ---: --- Minimum Chi^2 GST --- Sum of Chi^2 = 833.614 (861 data params - 31 model params = expected mean of 830; p-value = 0.458213) Completed in 2.4s 2*Delta(log(L)) = 833.943 Iteration 4 took 2.4s --- Iterative MLGST: Iter 5 of 5 1282 gate strings ---: --- Minimum Chi^2 GST --- Sum of Chi^2 = 1262.33 (1281 data params - 31 model params = expected mean of 1250; p-value = 0.397808) Completed in 1.2s 2*Delta(log(L)) = 1262.98 Iteration 5 took 1.2s Switching to ML objective (last iteration) --- MLGST --- Maximum log(L) = 631.455 below upper bound of -2.13633e+06 2*Delta(log(L)) = 1262.91 (1281 data params - 31 model params = expected mean of 1250; p-value = 0.393335) Completed in 0.7s 2*Delta(log(L)) = 1262.91 Final MLGST took 0.7s Iterative MLGST Total Time: 5.4s -- Performing 'single' gauge optimization on TP estimate -- -- Performing 'Spam 0.001' gauge optimization on TP estimate -- -- Performing 'Spam 0.001+v' gauge optimization on TP estimate -- -- Std Practice: Iter 2 of 3 (CPTP) --: --- Gate Sequence Creation --- 1282 sequences created Dataset has 3382 entries: 1282 utilized, 0 requested sequences were missing --- Iterative MLGST: Iter 1 of 5 92 gate strings ---: --- Minimum Chi^2 GST --- Sum of Chi^2 = 50.2614 (91 data params - 31 model params = expected mean of 60; p-value = 0.810701) Completed in 2.4s 2*Delta(log(L)) = 50.3385 Iteration 1 took 2.4s --- Iterative MLGST: Iter 2 of 5 168 gate strings ---: --- Minimum Chi^2 GST --- Sum of Chi^2 = 112.857 (167 data params - 31 model params = expected mean of 136; p-value = 0.926582) Completed in 1.8s 2*Delta(log(L)) = 112.882 Iteration 2 took 1.9s --- Iterative MLGST: Iter 3 of 5 450 gate strings ---: --- Minimum Chi^2 GST --- Sum of Chi^2 = 409.841 (449 data params - 31 model params = expected mean of 418; p-value = 0.602936) Completed in 3.1s 2*Delta(log(L)) = 410.036 Iteration 3 took 3.1s --- Iterative MLGST: Iter 4 of 5 862 gate strings ---: --- Minimum Chi^2 GST --- Sum of Chi^2 = 833.614 (861 data params - 31 model params = expected mean of 830; p-value = 0.458214) Completed in 2.2s 2*Delta(log(L)) = 833.943 Iteration 4 took 2.3s --- Iterative MLGST: Iter 5 of 5 1282 gate strings ---: --- Minimum Chi^2 GST --- Sum of Chi^2 = 1262.33 (1281 data params - 31 model params = expected mean of 1250; p-value = 0.397805) Completed in 1.8s 2*Delta(log(L)) = 1262.98 Iteration 5 took 1.8s Switching to ML objective (last iteration) --- MLGST --- Maximum log(L) = 631.455 below upper bound of -2.13633e+06 2*Delta(log(L)) = 1262.91 (1281 data params - 31 model params = expected mean of 1250; p-value = 0.393331) Completed in 1.4s 2*Delta(log(L)) = 1262.91 Final MLGST took 1.4s Iterative MLGST Total Time: 12.9s -- Performing 'single' gauge optimization on CPTP estimate -- -- Performing 'Spam 0.001' gauge optimization on CPTP estimate -- -- Performing 'Spam 0.001+v' gauge optimization on CPTP estimate -- -- Std Practice: Iter 3 of 3 (Target) --: --- Gate Sequence Creation --- 1282 sequences created Dataset has 3382 entries: 1282 utilized, 0 requested sequences were missing -- Performing 'single' gauge optimization on Target estimate -- -- Performing 'Spam 0.001' gauge optimization on Target estimate -- -- Performing 'Spam 0.001+v' gauge optimization on Target estimate -- *** Creating workspace *** *** Generating switchboard *** Found standard clifford compilation from std1Q_XYI Found standard clifford compilation from std1Q_XYI Found standard clifford compilation from std1Q_XYI Found standard clifford compilation from std1Q_XYI Found standard clifford compilation from std1Q_XYI Found standard clifford compilation from std1Q_XYI *** Generating tables *** *** Generating plots *** Statistical hypothesis tests did NOT find inconsistency between the datasets at 5.00% significance. The datasets are INCONSISTENT at 5.00% significance. - Details: - The aggregate log-likelihood ratio test is significant at 20.30 standard deviations. - The aggregate log-likelihood ratio test standard deviations signficance threshold is 1.98 - The number of sequences with data that is inconsistent is 14 - The maximum SSTVD over all sequences is 0.15 - The maximum SSTVD was observed for Gx(Gi)^4 The datasets are INCONSISTENT at 5.00% significance. - Details: - The aggregate log-likelihood ratio test is significant at 20.30 standard deviations. - The aggregate log-likelihood ratio test standard deviations signficance threshold is 1.98 - The number of sequences with data that is inconsistent is 14 - The maximum SSTVD over all sequences is 0.15 - The maximum SSTVD was observed for Gx(Gi)^4 Statistical hypothesis tests did NOT find inconsistency between the datasets at 5.00% significance. *** Merging into template file *** Output written to tutorial_files/exampleMultiDataSetReport directory Opening tutorial_files/exampleMultiDataSetReport/main.html... *** Report Generation Complete! Total time 262.564s ***
<pygsti.report.workspace.Workspace at 0x12c093eb8>
create_standard_report
options¶Finally, let us highlight a few of the additional arguments one can supply to create_standard_report
that allows further control over what gets reported.
Setting the link_to
argument to a tuple of 'pkl'
, 'tex'
, and/or 'pdf'
will create hyperlinks within the plots or below the tables of the HTML linking to Python pickle, LaTeX source, and PDF versions of the content, respectively. The Python pickle files for tables contain pickled pandas DataFrame
objects, wheras those of plots contain ordinary Python dictionaries of the data that is plotted. Applies to HTML reports only.
Setting the brevity
argument to an integer higher than $0$ (the default) will reduce the amount of information included in the report (for details on what is included for each value, see the doc string). Using brevity > 0
will reduce the time required to create, and later load, the report, as well as the output file/folder size. This applies to both HTML and PDF reports.
Below, we demonstrate both of these options in very brief (brevity=4
) report with links to pickle and PDF files. Note that to generate the PDF files you must have pdflatex
installed.
pygsti.report.create_standard_report(results_std,
"tutorial_files/exampleBriefReport",
title="Example Brief Report",
auto_open=True, verbosity=1,
brevity=4, link_to=('pkl','pdf'))
*** Creating workspace *** *** Generating switchboard *** Found standard clifford compilation from std1Q_XYI Found standard clifford compilation from std1Q_XYI Found standard clifford compilation from std1Q_XYI *** Generating tables *** *** Generating plots *** *** Merging into template file *** Output written to tutorial_files/exampleBriefReport directory Opening tutorial_files/exampleBriefReport/main.html... *** Report Generation Complete! Total time 138.453s ***
<pygsti.report.workspace.Workspace at 0x149ccea58>
create_report_notebook
¶In addition to the standard HTML-page reports demonstrated above, pyGSTi is able to generate a Jupyter notebook containing the Python commands to create the figures and tables within a general report. This is facilitated
by Workspace
objects, which are factories for figures and tables (see previous tutorials). By calling create_report_notebook
, all of the relevant Workspace
initialization and calls are dumped to a new notebook file, which can be run (either fully or partially) by the user at their convenience. Creating such "report notebooks" has the advantage that the user may insert Python code amidst the figure and table generation calls to inspect or modify what is display in a highly customizable fashion. The chief disadvantages of report notebooks is that they require the user to 1) have a Jupyter server up and running and 2) to run the notebook before any figures are displayed.
The line below demonstrates how to create a report notebook using create_report_notebook
. Note that the argument list is very similar to create_general_report
.
pygsti.report.create_report_notebook(results, "tutorial_files/exampleReport.ipynb",
title="GST Example Report Notebook", confidenceLevel=None,
auto_open=True, connected=False, verbosity=3)
Report Notebook created as tutorial_files/exampleReport.ipynb