Examples of tables and plots available from a Workspace

Examples of tables and plots available from a `Workspace`#

PyGSTi’s Workspace object is first a foremost a container and factory for plots and tables. At the most basic level, it can be used to generate nice output based on quantities (e.g. Model, DataSet, etc. objects) that you’ve computed or loaded within a notebook. For this, it’s useful to call init_notebook_mode with autodisplay=True (see below) so that you don’t have to .display() everything - display() gets called automatically when a plot or table is created.

Getting some results#

First, let’s run gate set tomography (GST) on the standard 1-qubit model to get some results to play with. We generate a few DataSet objects and then call run_long_sequence_gst to run GST, generating a ModelEstimateResults object (essentially a container for Model objects). For more details, see the tutorials GST overview tutorial, the tutorial on GST functions, and the tutorial explaining the ModelEstimateResults object.

import numpy as np
import pygsti
from pygsti.modelpacks import smq1Q_XYI

#The usual GST setup: we're going to run GST on the standard XYI 1-qubit model
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]
listOfExperiments = pygsti.circuits.create_lsgst_circuits(
    target_model.operations.keys(), prep_fiducials, meas_fiducials, germs, maxLengths)

#Create some datasets for analysis
mdl_datagen1 = target_model.depolarize(op_noise=0.1, spam_noise=0.02)
mdl_datagen2 = target_model.depolarize(op_noise=0.05, spam_noise=0.01).rotate(rotate=(0.01,0.01,0.01))

ds1 = pygsti.data.simulate_data(mdl_datagen1, listOfExperiments, num_samples=1000,
                                            sample_error="binomial", seed=1234)
ds2 = pygsti.data.simulate_data(mdl_datagen2, listOfExperiments, num_samples=1000,
                                            sample_error="binomial", seed=1234)
ds3 = ds1.copy_nonstatic(); ds3.add_counts_from_dataset(ds2); ds3.done_adding_data()

#Run GST on all three datasets
target_model.set_all_parameterizations("full TP")
results1 = pygsti.run_long_sequence_gst(ds1, target_model, prep_fiducials, meas_fiducials, germs, maxLengths, verbosity=0)
results2 = pygsti.run_long_sequence_gst(ds2, target_model, prep_fiducials, meas_fiducials, germs, maxLengths, verbosity=0)
results3 = pygsti.run_long_sequence_gst(ds3, target_model, prep_fiducials, meas_fiducials, germs, maxLengths, verbosity=0)

#make some shorthand variable names for later
tgt = results1.estimates['GateSetTomography'].models['target']

ds1 = results1.dataset
ds2 = results2.dataset
ds3 = results3.dataset

mdl1 = results1.estimates['GateSetTomography'].models['stdgaugeopt']
mdl2 = results2.estimates['GateSetTomography'].models['stdgaugeopt']
mdl3 = results3.estimates['GateSetTomography'].models['stdgaugeopt']

gss = results1.circuit_lists['final']

Gallery of `Workspace` plots and tables.#

Now that we have some results, let’s create a Workspace and make some plots and tables.

To get tables and plots to display properly, one must run init_notebook_mode. The connected argument indicates whether you want to rely on an active internet connection. If True, then resources will be loaded from the web (e.g. a CDN), and if you save a notebook as HTML the file size may be smaller. If False, then all the needed resources (except MathJax) are provided by pyGSTi, and an offline directory is automatically created in the same directory as your notebook. This directory contains all the necessary resources, and must “tag along” with the notebook and any saved-as-HTML versions of it in order for everything to work. The second argument, autodisplay, determines whether tables and plots are automatically displayed when they are created. If autodisplay=False, one must call the display() member function of a table or plot to display it.

from pygsti.report import workspace
w = workspace.Workspace()
w.init_notebook_mode(connected=False, autodisplay=True) 

Plots and tables are created via member functions of a Workspace (w in our case). Note that you can start typing “w.” and TAB-complete to see the different things a Workspace can make for you. Furthermore, pressing SHIFT-TAB after the opening parenthesis of a function, e.g. after typing “w.GatesVsTargetTable(”, will bring up Jupyter’s help window showing you the function signature (the arguments you need to give the function).

The remainder of this tutorial demonstrates some of the tables and plots you can create.#

Note that displayed objects have a resize handle in their lower right corner.

w.ColorBoxPlot(("logl",), gss, ds1, mdl1, typ='scatter')
w.ColorBoxPlot(("logl",), gss, ds1, mdl1, typ='boxes')
w.ColorBoxPlot(("logl",), gss, ds1, mdl1, typ='histogram')

iteration_estimates = [results1.estimates['GateSetTomography'].models['iteration %d estimate' % i]
                       for i in range(results1.estimates['GateSetTomography'].num_iterations)]
w.FitComparisonBarPlot(gss, results1.circuit_lists['iteration'],iteration_estimates, ds1)

w.GramMatrixBarPlot(ds1,tgt)

w.GatesVsTargetTable(mdl1, tgt)

w.SpamVsTargetTable(mdl2, tgt)

w.ColorBoxPlot(("chi2","logl"), gss, ds1, mdl1, box_labels=True)
  #Notice how long it takes to switch between "chi2" and "logl".  This 
  # is due to drawing all of the box labels (box_labels=True).

#This one requires knowng that each Results object holds a list of models
# from each GST intation along with the corresponding operation sequences that were used.
iteration_estimates = [results1.estimates['GateSetTomography'].models['iteration %d estimate' % i]
                       for i in range(results1.estimates['GateSetTomography'].num_iterations)]
w.FitComparisonTable(gss, results1.circuit_lists['iteration'], iteration_estimates, ds1)

# We can reuse 'gss' for all three since the operation sequences are the same.
w.FitComparisonTable(["GS1","GS2","GS3"], [gss, gss, gss], [mdl1,mdl2,mdl3], ds1, x_label="Model")

w.ChoiTable(mdl3, display=('matrix','barplot'))

w.GateMatrixPlot(mdl1[('Gxpi2',0)].to_dense(),scale=1.0, box_labels=True,ylabel="hello")
w.GateMatrixPlot(pygsti.tools.error_generator(mdl1[('Gxpi2',0)].to_dense(), tgt[('Gxpi2',0)].to_dense(), 'pp'), scale=1.5)

from pygsti.modelpacks import smq2Q_XYCNOT
w.GateMatrixPlot(smq2Q_XYCNOT.target_model()[('Gxpi2',0)].to_dense(),scale=1.0, box_labels=False,ylabel="hello",mx_basis_x="pp")

mx = np.array( 
[[ 7.3380823,   8.28446943,  7.4593754,   3.91256384,  0.68631199],
 [ 3.36139818,  7.42955114,  6.78516082,  0.35863173,  5.57713093],
 [ 2.61489939,  3.40182958,  6.77389064,  9.29736475,  0.33824271],
 [ 9.64258149,  9.45928809,  6.91516602,  5.61423854,  0.56480777],
 [ 2.15195669,  9.37588783,  5.1781991,   7.20087591,  1.46096288]], 'd')
cMap = pygsti.report.colormaps.LinlogColormap(vmin=0, vmax=10, num_boxes=25, pcntle=0.55, dof_per_box=1, color='blue')
w.MatrixPlot(mx, colormap=cMap, colorbar=False)

mx = np.identity(3,'d')
mx[0,1] = 2.1
mx[2,2] = 4.0
mx[2,0] = 3.0
mx[0,2] = 7.0
mx[2,1] = 10.0
mx[0,0] = np.nan
cMap = pygsti.report.colormaps.PiecewiseLinearColormap(
            [[0,(0,0.5,0)],[1,(0,1.0,0)],[2,(1.0,1.0,0)],
             [4,(1.0,0.5,0)],[10,(1.0,0,0)]])
#print(cMap.colorscale())
w.MatrixPlot(mx, colormap=cMap, colorbar=False, grid="white:1", box_labels=True, prec=2,
             xlabels=('full TP',"CPTPLND","full"),ylabels=("DS0","DS1","DS2"))

w.ErrgenTable(mdl3,tgt)

w.PolarEigenvaluePlot([np.linalg.eigvals(mdl2[('Gxpi2',0)].to_dense())],["purple"],scale=1.5)

w.GateEigenvalueTable(mdl2, display=('evals','polar'))

w.GateDecompTable(mdl1,target_model)
#w.old_GateDecompTable(gs1) #historical; 1Q only

#Note 2Q angle decompositions
from pygsti.modelpacks import smq2Q_XXYYII
from pygsti.modelpacks import smq2Q_XYCNOT

w.GateDecompTable(smq2Q_XXYYII.target_model(), smq2Q_XXYYII.target_model())

import scipy
I = np.array([[1,0],[0,1]],'complex')
X = np.array([[0,1],[1,0]],'complex')
Y = np.array([[0,1j],[-1j,0]],'complex')
XX = np.kron(X,X)
YY = np.kron(Y,Y)
IX = np.kron(I,X)
XI = np.kron(X,I)
testU = scipy.linalg.expm(-1j*np.pi/2*XX)
testS = pygsti.unitary_to_process_mx(testU)
testS = pygsti.change_basis(testS,"std","pp")

#mdl_decomp = std2Q_XYCNOT.target_model()
#mdl_decomp.operations['Gtest'] = testS
#w.GateDecompTable(mdl_decomp, mdl_decomp)

dsLabels = ["A","B","C"]
datasets = [ds1, ds2, ds3]
dscmps = {}
for i,ds_a in enumerate(datasets):
    for j,ds_b in enumerate(datasets[i+1:],start=i+1):
        dscmps[(i,j)] = pygsti.data.DataComparator([ds_a, ds_b])

w.DatasetComparisonSummaryPlot(dsLabels, dscmps)

w.DatasetComparisonHistogramPlot(dscmps[(1,2)])

Saving figures to file#

You can also save plot and figures to separate files using their saveas method. The output format is determined by the file extension, and allowed extensions are:

‘pdf’: Adobe portable document format
‘tex’: LaTeX source (uncompiled, tables only)
‘pkl’: Python pickle (of a pandas DataFrame for tables, a dict for plots)
‘html’: A stand-alone HTML document

import os
if not os.path.exists("../../tutorial_files/tempTest"):
    os.mkdir("../../tutorial_files/tempTest")

obj = w.GatesVsTargetTable(mdl1, tgt)
#obj = w.ErrgenTable(mdl3,tgt)
#obj = w.ColorBoxPlot(("logl",), gss, ds1, mdl1, typ='boxes')

obj.saveas("../../tutorial_files/tempTest/testSave.tex")
obj.saveas("../../tutorial_files/tempTest/testSave.pkl")
obj.saveas("../../tutorial_files/tempTest/testSave.html")

Saving as a pdf requires having pdflatex installed and on the system path.

obj.saveas("../../tutorial_files/tempTest/testSave.pdf")

Exporting notebooks to HTML#

If you want, you can save figure-containing notebooks (like this one) as an HTML file by going to File => Download As => HTML in the Jupyter menu. The resulting file will retain all of the plot interactivity, so long as its in a directory with an offline folder (because we set connected=False above).