Workspace Pickling

This example notebook shows how to generate reports with a pickled workspace object.
First, we run GST:

In [1]:
import pygsti
import pickle
from pygsti.construction import std1Q_XYI

gs_target = std1Q_XYI.gs_target
fiducials = std1Q_XYI.fiducials
germs = std1Q_XYI.germs
maxLengths = [1,2,4]
#maxLengths = [1, 2, 4, 8, 16, 32, 64]

#Generate some data
gs_datagen = gs_target.depolarize(gate_noise=0.1, spam_noise=0.001)
gs_datagen = gs_datagen.rotate(rotate=0.04)
listOfExperiments = pygsti.construction.create_lsgst_circuits(gs_target, fiducials, fiducials, germs, maxLengths)
ds = pygsti.construction.simulate_data(gs_datagen, listOfExperiments, nSamples=1000,
                                            sampleError="binomial", seed=1234)
#Run GST
gs_target.set_all_parameterizations("TP") #TP-constrained
results = pygsti.run_long_sequence_gst(ds, gs_target, fiducials, fiducials, germs,
                                      maxLengths, verbosity=0)
with open('example_files/example_report_results.pkl', 'wb') as outfile:
    pickle.dump(results, outfile, protocol=2)

# Case1: TP-constrained GST
tpTarget = gs_target.copy()
tpTarget.set_all_parameterizations("TP")
results_tp = pygsti.run_long_sequence_gst(ds, tpTarget, fiducials, fiducials, germs,
                                      maxLengths, gaugeOptParams=False, verbosity=0)
# Gauge optimize
est = results_tp.estimates['default']
gsFinal = est.gatesets['final iteration estimate']
gsTarget = est.gatesets['target']
for spamWt in [1e-4,1e-3,1e-2,1e-1,1.0]:
    gs = pygsti.gaugeopt_to_target(gsFinal,gsTarget,{'gates':1, 'spam':spamWt})
    est.add_gaugeoptimized({'itemWeights': {'gates':1, 'spam':spamWt}}, gs, "Spam %g" % spamWt) 

#Case2: "Full" GST
fullTarget = gs_target.copy()
fullTarget.set_all_parameterizations("full")
results_full = pygsti.run_long_sequence_gst(ds, fullTarget, fiducials, fiducials, germs,
                                      maxLengths, gaugeOptParams=False, verbosity=0)
#Gauge optimize
est = results_full.estimates['default']
gsFinal = est.gatesets['final iteration estimate']
gsTarget = est.gatesets['target']
for spamWt in [1e-4,1e-3,1e-2,1e-1,1.0]:
    gs = pygsti.gaugeopt_to_target(gsFinal,gsTarget,{'gates':1, 'spam':spamWt})
    est.add_gaugeoptimized({'itemWeights': {'gates':1, 'spam':spamWt}}, gs, "Spam %g" % spamWt)

with open('example_files/full_report_results.pkl', 'wb') as outfile:
    pickle.dump((results_tp, results_full), outfile, protocol=2)

Workspace Pickling

Now that we've saved some GST data, we can begin generating reports.
The smart cache will track which plots we are unable to pickle.
At the time of writing, these are only plotly plots. ReportTable objects should delete their ws and figs members, allowing themselves to be pickled.

In [2]:
from pygsti.tools import timed_block

with timed_block('pickle loading'):
    with open('example_files/full_report_results.pkl', 'rb') as infile:
        results_tp, results_full = pickle.load(infile)
with timed_block('TP/Full multi report'):
    ws = pygsti.report.create_general_report({'TP': results_tp, "Full": results_full},
                                             "tutorial_files/exampleMultiGenReport.html",
                                             "Workspace Pickling Example Report1", 
                                             verbosity=3, auto_open=False)
ws.save_cache('example_files/wscache.pkl', True)
pickle loading took 0.027332 seconds

*** Creating workspace ***
*** Generating switchboard tables ***
*** Generating plots ***
*** Merging into template file ***
Output written to tutorial_files/exampleMultiGenReport.html
TP/Full multi report took 4.755988 seconds

Unpickled keys:
set()

In the Cell below, we create the same report, but use the existing workspace object.

In [3]:
with timed_block('TP/Full multi report'):
    ws = pygsti.report.create_general_report({'TP': results_tp, "Full": results_full},
                                             "tutorial_files/exampleMultiGenReport.html",
                                             "Workspace Pickling Example Report2", 
                                             verbosity=3, auto_open=False, ws=ws)
*** Creating workspace ***
*** Generating switchboard tables ***
*** Generating plots ***
*** Merging into template file ***
Output written to tutorial_files/exampleMultiGenReport.html
TP/Full multi report took 1.400488 seconds

However, most of the time, we care about the time of the full report generation progress:

In [4]:
del ws
with timed_block('Full report creation from pickle'):
    ws = pygsti.report.Workspace('example_files/wscache.pkl')
    pygsti.report.create_general_report({'TP': results_tp, "Full": results_full},
                                        "tutorial_files/exampleMultiGenReport.html",
                                        "Workspace Pickling Example Report3", 
                                        verbosity=3, auto_open=False, ws=ws)
*** Creating workspace ***
*** Generating switchboard tables ***
*** Generating plots ***
*** Merging into template file ***
Output written to tutorial_files/exampleMultiGenReport.html
Full report creation from pickle took 1.527072 seconds

In [ ]: