--- jupytext: text_representation: extension: .md format_name: myst format_version: 0.13 jupytext_version: 1.19.4 kernelspec: display_name: Python 3 (ipykernel) language: python name: python3 --- # 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](../gst/Overview), the [tutorial on GST functions](../gst/Driverfunctions), and the [tutorial explaining the ModelEstimateResults object](../objects/Results). ```{code-cell} ipython3 import numpy as np import pygsti from pygsti.modelpacks import smq1Q_XYI ``` ```{code-cell} ipython3 #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) ``` ```{code-cell} ipython3 #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() ``` ```{code-cell} ipython3 #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. ```{code-cell} ipython3 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. ```{code-cell} ipython3 w.ColorBoxPlot(("logl",), gss, ds1, mdl1, typ='scatter') w.ColorBoxPlot(("logl",), gss, ds1, mdl1, typ='boxes') w.ColorBoxPlot(("logl",), gss, ds1, mdl1, typ='histogram') ``` ```{code-cell} ipython3 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) ``` ```{code-cell} ipython3 w.GramMatrixBarPlot(ds1,tgt) ``` ```{code-cell} ipython3 w.GatesVsTargetTable(mdl1, tgt) ``` ```{code-cell} ipython3 w.SpamVsTargetTable(mdl2, tgt) ``` ```{code-cell} ipython3 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). ``` ```{code-cell} ipython3 #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) ``` ```{code-cell} ipython3 # 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") ``` ```{code-cell} ipython3 w.ChoiTable(mdl3, display=('matrix','barplot')) ``` ```{code-cell} ipython3 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) ``` ```{code-cell} ipython3 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") ``` ```{code-cell} ipython3 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) ``` ```{code-cell} ipython3 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")) ``` ```{code-cell} ipython3 w.ErrgenTable(mdl3,tgt) ``` ```{code-cell} ipython3 w.PolarEigenvaluePlot([np.linalg.eigvals(mdl2[('Gxpi2',0)].to_dense())],["purple"],scale=1.5) ``` ```{code-cell} ipython3 w.GateEigenvalueTable(mdl2, display=('evals','polar')) ``` ```{code-cell} ipython3 w.GateDecompTable(mdl1,target_model) #w.old_GateDecompTable(gs1) #historical; 1Q only ``` ```{code-cell} ipython3 #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) ``` ```{code-cell} ipython3 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) ``` ```{code-cell} ipython3 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 ```{code-cell} ipython3 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. ```{code-cell} ipython3 :tags: [nbval-skip] 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).