# Report Generation Tutorial¶

PyGSTi is able to construct polished report documents, which provide high-level summaries as well as detailed analyses of results (Gate Set Tomography (GST) and model-testing results in particular). Reports are intended to be quick and easy way of analyzing Model-type estimates, and pyGSTi's report generation functions are specifically designed to interact with the ModelEstimateResults object (producted by several high-level algorithm functions - see, for example, the GST overview tutorial and GST functions tutorial.). The report generation functions in pyGSTi takes one or more results (often ModelEstimateResults-type) objects as input and produces an HTML file as output. The HTML format allows the reports to include interactive plots and switches (see the workspace switchboard tutorial, making it easy to compare different types of analysis or data sets.

PyGSTi's reports are stand-alone HTML documents which cannot run Python. Thus, all the results displayed in a report must be pre-computed (in Python). If you find yourself wanting to fiddle with things and feel that these reports are too static, please consider using a Workspace object (see the Workspace tutorial) within a Jupyter notebook, where you can intermix report tables/plots and Python. Internally, functions like construct_standard_report (see below) are simple factories for Report objects, which are in turn little more than a wrapper around a Workspace object and a set of instructions for how to generate reports of different output formats.

### Get some ModelEstimateResults¶

We start by performing GST using do_long_sequence_gst, as usual, to create a ModelEstimateResults object (we could also have just loaded one from file). See the GST functions tutorial for more details.

In [ ]:
import pygsti
from pygsti.modelpacks import smq1Q_XYI

target_model = smq1Q_XYI.target_model()
prep_fiducials = smq1Q_XYI.prep_fiducials()
meas_fiducials = smq1Q_XYI.meas_fiducials()
germs = smq1Q_XYI.germs()
maxLengths = [1,2,4,8,16]

#Run GST
target_model.set_all_parameterizations("TP") #TP-constrained
results = pygsti.do_long_sequence_gst(ds, target_model, prep_fiducials, meas_fiducials, germs,
maxLengths, verbosity=3)


### Make a report¶

Now that we have results, we use the construct_standard_report method within pygsti.report to generate a Report.
pygsti.report.construct_standard_report is the most commonly used report factory function in pyGSTi, as it is appropriate for smaller models (1- and 2-qubit) which have operations that are or can be represeted as dense matrices and/or vectors.

Once constructed, a Report object can be used to write a report as an HTML document, PDF, or notebook. 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.

In [ ]:
report = pygsti.report.construct_standard_report(results, title="GST Example Report", verbosity=1)
#HTML
report.write_html("../tutorial_files/exampleReport", auto_open=True, verbosity=1)

print("\n")

#PDF
report.write_pdf("../tutorial_files/exampleReport.pdf", auto_open=True, verbosity=1)


There are several remarks about these reports worth noting:

1. The HTML reports are the primary report type in pyGSTi, and are much more flexible. The PDF reports are more limited (they can only display a single estimate and gauge optimization), and essentially contain a subset of the information and descriptive text of a HTML report. So, if you can, use the HTML reports. The PDF report's strength is its portability: PDFs are easily displayed by many devices, and they embed all that they need neatly into a single file. If you need to generate a PDF report from 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.
2. It's best to use Firefox when opening the HTML reports. (If there's a problem with your brower's capabilities it will be shown on the screen when you try to load the report.)
3. You'll need pdflatex on your system to compile PDF reports.
4. To familiarize yourself with the layout of an HTML report, click on the gray "Help" link on the black sidebar.

### Multiple estimates in a single report¶

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 gauge_opt_params=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.

In [ ]:
#Case1: TP-constrained GST
tpTarget = target_model.copy()
tpTarget.set_all_parameterizations("TP")
results_tp = pygsti.do_long_sequence_gst(ds, tpTarget, prep_fiducials, meas_fiducials, germs,
maxLengths, gauge_opt_params=False, verbosity=1)

#Gauge optimize
est = results_tp.estimates['GateSetTomography']
mdlFinal = est.models['final iteration estimate']
mdlTarget = est.models['target']
for spamWt in [1e-4,1e-2,1.0]:
mdl = pygsti.gaugeopt_to_target(mdlFinal,mdlTarget,{'gates':1, 'spam':spamWt})
est.add_gaugeoptimized({'item_weights': {'gates':1, 'spam':spamWt}}, mdl, "Spam %g" % spamWt)

In [ ]:
#Case2: "Full" GST
fullTarget = target_model.copy()
fullTarget.set_all_parameterizations("full")
results_full = pygsti.do_long_sequence_gst(ds, fullTarget, prep_fiducials, meas_fiducials, germs,
maxLengths, gauge_opt_params=False, verbosity=1)

#Gauge optimize
est = results_full.estimates['GateSetTomography']
mdlFinal = est.models['final iteration estimate']
mdlTarget = est.models['target']
for spamWt in [1e-4,1e-2,1.0]:
mdl = pygsti.gaugeopt_to_target(mdlFinal,mdlTarget,{'gates':1, 'spam':spamWt})
est.add_gaugeoptimized({'item_weights': {'gates':1, 'spam':spamWt}}, mdl, "Spam %g" % spamWt)


We'll now call the same construct_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.

In [ ]:
ws = pygsti.report.Workspace()
report = pygsti.report.construct_standard_report(
{'TP': results_tp, "Full": results_full}, title="Example Multi-Estimate Report", ws=ws, verbosity=2)
report.write_html("../tutorial_files/exampleMultiEstimateReport", auto_open=True, verbosity=2)


In the above call we construct 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. A Workspace object can optionally be passed as an argument to construct_standard_report, where it is used to create all 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. By passing a preconstructed Workspace object to construct_standard_report, we allow it to utilize previously cached quantities.

Another way: Because both results_tp and results_full above used the same dataset and operation sequences, we could have combined them as two estimates in a single ModelEstimateResults object (see the previous tutorial on pyGSTi's results objects). 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:

In [ ]:
results_tp.rename_estimate('GateSetTomography','TP')
results_full.rename_estimate('GateSetTomography','Full')
results_both = results_tp.copy() #copy just for neatness


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 construct_standard_report with the same Workspace we previously used to generate a report. This tells the constructed Report 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.

In [ ]:
pygsti.report.construct_standard_report(
results_both,
title="Example Multi-Estimate Report (v2)",
ws=ws, verbosity=2
).write_html("../tutorial_files/exampleMultiEstimateReport2", auto_open=True, verbosity=2)


### Multiple estimates and 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, and see the GST functions tutorial). 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.

In [ ]:
results_std = pygsti.do_stdpractice_gst(ds, target_model, prep_fiducials, meas_fiducials, germs,
maxLengths, verbosity=4, modes="TP,CPTP,Target",
gauge_opt_suite=('stdgaugeopt','toggleValidSpam'))

# Generate a report with "TP", "CPTP", and "Target" estimates
pygsti.report.construct_standard_report(
results_std, title="Post StdPractice Report", verbosity=1
).write_html("../tutorial_files/exampleStdReport", auto_open=True, verbosity=1)


### Reports with confidence regions¶

To display confidence intervals for reported quantities, you must do two things:

1. you must specify the confidenceLevel argument to construct_standard_report.
2. the estimate(s) being reported must have a valid confidence-region-factory.

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.

In [ ]:
#Construct and initialize a "confidence region factory" for the CPTP estimate
crfact.compute_hessian(comm=None) #we could use more processors
crfact.project_hessian('intrinsic error')

pygsti.report.construct_standard_report(
results_std, title="Post StdPractice Report (w/CIs on CPTP)",
confidenceLevel=95, verbosity=1
).write_html("../tutorial_files/exampleStdReport2", auto_open=True, verbosity=1)


### Reports with multiple different data sets¶

We've already seen above that construct_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 DataSets (each Results object only holds estimates for a single DataSet). Furthermore, when the data sets have the same operation 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.

In [ ]:
#Make another dataset & estimates
depol_gateset = target_model.depolarize(op_noise=0.1)
datagen_gateset = depol_gateset.rotate((0.05,0,0.03))

#Compute the sequences needed to perform Long Sequence GST on
# this Model with sequences up to lenth 512
circuit_list = pygsti.construction.make_lsgst_experiment_list(
smq1Q_XYI.target_model(), smq1Q_XYI.prep_fiducials(), smq1Q_XYI.meas_fiducials(),
smq1Q_XYI.germs(), [1,2,4,8,16,32,64,128,256,512])
ds2 = pygsti.construction.generate_fake_data(datagen_gateset, circuit_list, n_samples=1000,
sample_error='binomial', seed=2018)
results_std2 = pygsti.do_stdpractice_gst(ds2, target_model, prep_fiducials, meas_fiducials, germs,
maxLengths, verbosity=3, modes="TP,CPTP,Target",
gauge_opt_suite=('stdgaugeopt','toggleValidSpam'))

pygsti.report.construct_standard_report(
{'DS1': results_std, 'DS2': results_std2},
title="Example Multi-Dataset Report", verbosity=1
).write_html("../tutorial_files/exampleMultiDataSetReport", auto_open=True, verbosity=1)


### Other cool Report tricks¶

Finally, let us highlight a few of the additional arguments one can supply to the respective Report output methods that allows further control over what gets included in the generated report.

• 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.

In [ ]:
pygsti.report.construct_standard_report(
results_std, title="Example Brief Report", verbosity=1
).write_html("../tutorial_files/exampleBriefReport", auto_open=True, verbosity=1,


## Advanced Reports: Report.write_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 Report.write_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 write_notebook. Note that the argument list is very similar to the other Report output methods.

In [ ]:
pygsti.report.construct_standard_report(
results, title="GST Example Report Notebook", confidenceLevel=None, verbosity=3
).write_notebook("../tutorial_files/exampleReport.ipynb", auto_open=True, connected=False, verbosity=3)


## Multi-qubit reports¶

The dimension of the density matrix space with with more than 2 qubits starts to become quite large, and Models for 3+ qubits rarely allow every element of the operation process matrices to vary independently. As such, many of the figures generated by construct_standard_report are both too unwieldy (displaying a $64 \times 64$ grid of colored boxes for each operation) and not very helpful (you don't often care about what each element of an operation matrix is). For this purpose, we are developing a report that doesn't just dump out and analyze operation matrices as a whole, but looks at a Model's structure to determine how best to report quantities. This "n-qubit report" is invoked using pygsti.report.construct_nqnoise_report, and has similar arguments to construct_standard_report. It is, however still under development, and while you're welcome to try it out, it may crash or not work in other weird ways.