pygsti.report.workspaceplots
Classes corresponding to plots within a Workspace context.
Module Contents
Classes
Plot serving as a key for fiducial rows/columns of each plaquette of a circuit color box plot. 

Plot of colored boxes arranged into plaquettes showing various quanties for each circuit in an analysis. 

Plot of a operation matrix using colored boxes. 

Plot of a general matrix using colored boxes 

Polar plot of complex eigenvalues 

Plot of matrix of (usually errorgenerator) projections 

Bar plot of eigenvalues showing red bars for negative values 

Bar plot of Gram matrix eigenvalues stacked against those of target 

Bar plot showing the overall (aggregate) goodness of fit (along one dimension). 

Box plot showing the overall (aggregate) goodness of fit (along 2 dimensions). 

A grid of grayscale boxes comparing data sets pairwise. 

Histogram of pvalues comparing two data sets 

Stacked bar plot showing pergate reference values and wildcard budgets. 

Plot of RB Decay curve 
Attributes
 pygsti.report.workspaceplots.go_x_axis
 class pygsti.report.workspaceplots.BoxKeyPlot(ws, prep_fiducials, meas_fiducials, xlabel='Preparation fiducial', ylabel='Measurement fiducial', scale=1.0)
Bases:
pygsti.report.workspace.WorkspacePlot
Plot serving as a key for fiducial rows/columns of each plaquette of a circuit color box plot.
This plot shows the layout of a single subblock of a goodnessoffit box plot (such as those produced by ColorBoxPlot)
Parameters
 wsWorkspace
The containing (parent) workspace.
 prep_fiducialslist of Circuits
Preparation fiducials.
 meas_fiducialslist of Circuits
Measurement fiducials.
 xlabel: str, optional
Xaxis label
 ylabel: str, optional
Yaxis label
 scalefloat, optional
Scaling factor to adjust the size of the final figure.
Create a plot showing the layout of a single subblock of a goodnessoffit box plot (such as those produced by ColorBoxPlot)
Parameters
 prep_fiducials, meas_fiducialslist of Circuits
Preparation and measurement fiducials.
 xlabel, ylabelstr, optional
X and Y axis labels
 scalefloat, optional
Scaling factor to adjust the size of the final figure.
 class pygsti.report.workspaceplots.ColorBoxPlot(ws, plottype, circuits, dataset, model, sum_up=False, box_labels=False, hover_info=True, invert=False, prec='compact', linlg_pcntle=0.05, direct_gst_models=None, dscomparator=None, stabilityanalyzer=None, mdc_store=None, submatrices=None, typ='boxes', scale=1.0, comm=None, wildcard=None, colorbar=False, bgcolor='white', genericdict=None, genericdict_threshold=None)
Bases:
pygsti.report.workspace.WorkspacePlot
Plot of colored boxes arranged into plaquettes showing various quanties for each circuit in an analysis.
Parameters
 wsWorkspace
The containing (parent) workspace.
 plottype{“chi2”,”logl”,”tvd”,”blank”,”errorrate”,”dscmp”, “driftdetector”, “driftsize”}
Specifies the type of plot. “errorate” requires that direct_gst_models be set.
 circuitsCircuitList or list of Circuits
Specifies the set of circuits, usually along with their structure, e.g. fiducials, germs, and maximum lengths.
 datasetDataSet
The data used to specify frequencies and counts.
 modelModel
The model used to specify the probabilities and SPAM labels.
 sum_upbool, optional
False displays each matrix element as it’s own color box True sums the elements of each (x,y) matrix and displays a single color box for the sum.
 box_labelsbool, optional
Whether box labels are displayed. It takes much longer to generate the figure when this is set to True.
 hover_infobool, optional
Whether to include interactive hover labels.
 invertbool, optional
If True, invert the nesting order of the color box plot (applicable only when sum_up == False).
 precint, optional
Precision for box labels. Allowed values are: * ‘compact’ = round to nearest whole number using at most 3 characters * ‘compacthp’ = show as much precision as possible using at most 3 characters * int >= 0 = fixed precision given by int * int < 0 = number of significant figures given by int
 linlg_pcntlefloat, optional
Specifies the (1  linlg_pcntle) percentile to compute for the boxplots
 direct_gst_modelsdict, optional
A dictionary of “direct” Models used when displaying certain plot types. Keys are circuits and values are corresponding gate sets (see plottype above).
 dscomparatorDataComparator, optional
The data set comparator used to produce the “dscmp” plot type.
 stabilityanalyzerStabilityAnalyzer or 3tuple, optional
Only used to produce the “driftdetector” and “driftsize” boxplot. If a StabilityAnalyzer, then this contains the results of the drift / stability analysis to be displayed. For nonexpert users, this is the best option. If a tuple, then the first element of the tuple is this StabilityAnalyzer object, and the second and third elements of the tuple label which instability detection results to display (a StabilityAnalyzer can contain multiple distinct tests for instability). The second element is the “detectorkey”, which can be None (the default), or a string specifying which of the drift detection results to use for the plot. If it is None, then the default set of results are used. The third element of the tuple is either None, or a tuple that specifies which “level” of tests to use from the drift detection run (specified by the detectorkey), e.g., percircuit with outcomes averaged or percircuit peroutcome.
 submatricesdict, optional
A dictionary whose keys correspond to other potential plot types and whose values are each a listoflists of the sub matrices to plot, corresponding to the used x and y values of the structure of circuits.
 typ{“boxes”,”scatter”,”histogram”}
Which type of plot to make: the standard grid of “boxes”, a “scatter” plot of the values vs. sequence length, or a “histogram” of all the values.
 scalefloat, optional
Scaling factor to adjust the size of the final figure.
 commmpi4py.MPI.Comm, optional
When not None, an MPI communicator for distributing the computation across multiple processors.
 wildcardWildcardBudget
A wildcard budget to apply to the objective function that increases the goodness of fit by adjusting (by an amount measured in TVD) the probabilities produced by model before comparing with the frequencies in dataset. Currently, this functionality is only supported for plottype == “logl”.
 colorbarbool, optional
Whether to include a colorbar.
 bgcolorstr, optional
Background color for this plot. Can be common color names, e.g. “black”, or string RGB values, e.g. “rgb(255,128,0)”.
Create a plot displaying the value of percircuit quantities. TODO: docstring
Values are shown on a grid of colored boxes, organized according to the structure of the circuits (e.g. by germ and “L”).
Parameters
 plottype{“chi2”,”logl”,”tvd”,”blank”,”errorrate”,”dscmp”, “driftdetector”, “driftsize”}
Specifies the type of plot. “errorate” requires that direct_gst_models be set.
 circuitsCircuitList or list of Circuits
Specifies the set of circuits, usually along with their structure, e.g. fiducials, germs, and maximum lengths.
 datasetDataSet
The data used to specify frequencies and counts.
 modelModel
The model used to specify the probabilities and SPAM labels.
 sum_upbool, optional
False displays each matrix element as it’s own color box True sums the elements of each (x,y) matrix and displays a single color box for the sum.
 box_labelsbool, optional
Whether box labels are displayed. It takes much longer to generate the figure when this is set to True.
 hover_infobool, optional
Whether to include interactive hover labels.
 invertbool, optional
If True, invert the nesting order of the color box plot (applicable only when sum_up == False).
 precint, optional
 Precision for box labels. Allowed values are:
‘compact’ = round to nearest whole number using at most 3 characters ‘compacthp’ = show as much precision as possible using at most 3 characters int >= 0 = fixed precision given by int int < 0 = number of significant figures given by int
 linlg_pcntlefloat, optional
Specifies the (1  linlg_pcntle) percentile to compute for the boxplots
 direct_gst_modelsdict, optional
A dictionary of “direct” Models used when displaying certain plot types. Keys are circuits and values are corresponding gate sets (see plottype above).
 dscomparatorDataComparator, optional
The data set comparator used to produce the “dscmp” plot type.
 stabilityanalyzerStabilityAnalyzer or 3tuple, optional
Only used to produce the “driftdetector” and “driftsize” boxplot. If a StabilityAnalyzer, then this contains the results of the drift / stability analysis to be displayed. For nonexpert users, this is the best option. If a tuple, then the first element of the tuple is this StabilityAnalyzer object, and the second and third elements of the tuple label which instability detection results to display (a StabilityAnalyzer can contain multiple distinct tests for instability). The second element is the “detectorkey”, which can be None (the default), or a string specifying which of the drift detection results to use for the plot. If it is None, then the default set of results are used. The third element of the tuple is either None, or a tuple that specifies which “level” of tests to use from the drift detection run (specified by the detectorkey), e.g., percircuit with outcomes averaged or percircuit peroutcome.
 submatricesdict, optional
A dictionary whose keys correspond to other potential plot types and whose values are each a listoflists of the sub matrices to plot, corresponding to the used x and y values of the structure of circuits.
 typ{“boxes”,”scatter”,”histogram”}
Which type of plot to make: the standard grid of “boxes”, a “scatter” plot of the values vs. sequence length, or a “histogram” of all the values.
 scalefloat, optional
Scaling factor to adjust the size of the final figure.
 commmpi4py.MPI.Comm, optional
When not None, an MPI communicator for distributing the computation across multiple processors.
 wildcardWildcardBudget
A wildcard budget to apply to the objective function that increases the goodness of fit by adjusting (by an amount measured in TVD) the probabilities produced by model before comparing with the frequencies in dataset. Currently, this functionality is only supported for plottype == “logl”.
 colorbarbool, optional
Whether to include a colorbar.
 bgcolorstr, optional
Background color for this plot. Can be common color names, e.g. “black”, or string RGB values, e.g. “rgb(255,128,0)”.
 class pygsti.report.workspaceplots.GateMatrixPlot(ws, op_matrix, color_min=1.0, color_max=1.0, mx_basis=None, xlabel=None, ylabel=None, box_labels=False, colorbar=None, prec=0, mx_basis_y=None, scale=1.0, eb_matrix=None)
Bases:
pygsti.report.workspace.WorkspacePlot
Plot of a operation matrix using colored boxes.
More specific than
MatrixPlot
because of basis formatting for x and y labels.Parameters
 wsWorkspace
The containing (parent) workspace.
 op_matrixndarray
The operation matrix data to display.
 color_minfloat, optional
Minimum value of the color scale.
 color_maxfloat, optional
Maximum value of the color scale.
 mx_basisstr or Basis object, optional
The basis, often of op_matrix, used to create the xlabels (and ylabels when mx_basis_y is None). Typically in {“pp”,”gm”,”std”,”qt”}. If you don’t want labels, leave as None.
 xlabelstr, optional
A xaxis label for the plot.
 ylabelstr, optional
A yaxis label for the plot.
 box_labelsbool, optional
Whether box labels are displayed.
 colorbarbool optional
Whether to display a color bar to the right of the box plot. If None, then a colorbar is displayed when box_labels == False.
 precint or {‘compact’,’compacthp’}, optional
Precision for box labels. Only relevant when box_labels == True. Allowed values are:
‘compact’ = round to nearest whole number using at most 3 characters
‘compacthp’ = show as much precision as possible using at most 3 characters
int >= 0 = fixed precision given by int
int < 0 = number of significant figures given by int
 mx_basis_ystr or Basis object, optional
The basis, used to create the ylabels (for rows) when these are different from the xlabels. Typically in {“pp”,”gm”,”std”,”qt”}. If you don’t want labels, leave as None.
 scalefloat, optional
Scaling factor to adjust the size of the final figure.
 eb_matrixnumpy array, optional
An array, of the same size as op_matrix, which gives error bars to be be displayed in the hover info.
Creates a color box plot of a operation matrix using a diverging color map.
This can be a useful way to display large matrices which have so many entries that their entries cannot easily fit within the width of a page.
Parameters
 op_matrixndarray
The operation matrix data to display.
 color_min, color_maxfloat, optional
Min and max values of the color scale.
 mx_basisstr or Basis object, optional
The basis, often of op_matrix, used to create the xlabels (and ylabels when mx_basis_y is None). Typically in {“pp”,”gm”,”std”,”qt”}. If you don’t want labels, leave as None.
 xlabelstr, optional
A xaxis label for the plot.
 ylabelstr, optional
A yaxis label for the plot.
 box_labelsbool, optional
Whether box labels are displayed.
 colorbarbool optional
Whether to display a color bar to the right of the box plot. If None, then a colorbar is displayed when box_labels == False.
 precint or {‘compact’,’compacthp’}, optional
Precision for box labels. Only relevant when box_labels == True. Allowed values are:
‘compact’ = round to nearest whole number using at most 3 characters
‘compacthp’ = show as much precision as possible using at most 3 characters
int >= 0 = fixed precision given by int
int < 0 = number of significant figures given by int
 mx_basis_ystr or Basis object, optional
The basis, used to create the ylabels (for rows) when these are different from the xlabels. Typically in {“pp”,”gm”,”std”,”qt”}. If you don’t want labels, leave as None.
 scalefloat, optional
Scaling factor to adjust the size of the final figure.
 eb_matrixnumpy array, optional
An array, of the same size as op_matrix, which gives error bars to be be displayed in the hover info.
 class pygsti.report.workspaceplots.MatrixPlot(ws, matrix, color_min=1.0, color_max=1.0, xlabels=None, ylabels=None, xlabel=None, ylabel=None, box_labels=False, colorbar=None, colormap=None, prec=0, scale=1.0, grid='black')
Bases:
pygsti.report.workspace.WorkspacePlot
Plot of a general matrix using colored boxes
Parameters
 wsWorkspace
The containing (parent) workspace.
 matrixndarray
The operation matrix data to display.
 color_minfloat
Color scale minimum.
 color_maxfloat
Color scale maximum.
 xlabelslist, optional
List of (str) box labels along the xaxis.
 ylabelslist, optional
List of (str) box labels along the yaxis.
 xlabelstr, optional
A xaxis label for the plot.
 ylabelstr, optional
A yaxis label for the plot.
 box_labelsbool, optional
Whether box labels are displayed.
 colorbarbool optional
Whether to display a color bar to the right of the box plot. If None, then a colorbar is displayed when box_labels == False.
 colormapColormap, optional
A color map object used to convert the numerical matrix values into colors.
 precint or {‘compact’,’compacthp’}, optional
Precision for box labels. Only relevant when box_labels == True. Allowed values are:
‘compact’ = round to nearest whole number using at most 3 characters
‘compacthp’ = show as much precision as possible using at most 3 characters
int >= 0 = fixed precision given by int
int < 0 = number of significant figures given by int
 scalefloat, optional
Scaling factor to adjust the size of the final figure.
 grid{“white”,”black”,None}
What color grid lines, if any, to add to the plot. Advanced usage allows the addition of :N where N is an integer giving the line width.
Creates a color box plot of a matrix using the given color map.
This can be a useful way to display large matrices which have so many entries that their entries cannot easily fit within the width of a page.
Parameters
 matrixndarray
The operation matrix data to display.
 color_min, color_maxfloat, optional
Min and max values of the color scale.
 xlabels, ylabels: list, optional
List of (str) box labels for each axis.
 xlabelstr, optional
A xaxis label for the plot.
 ylabelstr, optional
A yaxis label for the plot.
 box_labelsbool, optional
Whether box labels are displayed.
 colorbarbool optional
Whether to display a color bar to the right of the box plot. If None, then a colorbar is displayed when box_labels == False.
 colormapColormap, optional
A color map object used to convert the numerical matrix values into colors.
 precint or {‘compact’,’compacthp’}, optional
Precision for box labels. Only relevant when box_labels == True. Allowed values are:
‘compact’ = round to nearest whole number using at most 3 characters
‘compacthp’ = show as much precision as possible using at most 3 characters
int >= 0 = fixed precision given by int
int < 0 = number of significant figures given by int
 scalefloat, optional
Scaling factor to adjust the size of the final figure.
 grid{“white”,”black”,None}
What color grid lines, if any, to add to the plot. Advanced usage allows the addition of :N where N is an integer giving the line width.
 class pygsti.report.workspaceplots.PolarEigenvaluePlot(ws, evals_list, colors, labels=None, scale=1.0, amp=None, center_text=None)
Bases:
pygsti.report.workspace.WorkspacePlot
Polar plot of complex eigenvalues
Parameters
 wsWorkspace
The containing (parent) workspace.
 evals_listlist
A list of eigenvalue arrays to display.
 colorslist
A corresponding list of color names to use for arrays given by evals_list (must have len(colors) == len(evals_list)). Colors can be standard names, e.g. “blue”, or rgb strings such as “rgb(23,92,64)”.
 labelslist, optional
A list of labels, one for each element of evals_list to be placed in the plot legend.
 scalefloat, optional
Scaling factor to adjust the size of the final figure.
 ampfloat, optional
An amount to amplify (raise to the exponent amp) each set of eigenvalues. (Amplified eigenvalues are shown in the same color but with smaller markers.) If amp is None, no amplification is performed.
 center_textstr, optional
Text to be placed at the very center of the polar plot (sometimes useful to use as a title).
Creates a polar plot of one or more sets of eigenvalues (or any complex #s).
Parameters
 evals_listlist
A list of eigenvalue arrays to display.
 colorslist
A corresponding list of color names to use for arrays given by evals_list (must have len(colors) == len(evals_list)). Colors can be standard names, e.g. “blue”, or rgb strings such as “rgb(23,92,64)”.
 labelslist, optional
A list of labels, one for each element of evals_list to be placed in the plot legend.
 scalefloat, optional
Scaling factor to adjust the size of the final figure.
 ampfloat, optional
An amount to amplify (raise to the exponent amp) each set of eigenvalues. (Amplified eigenvalues are shown in the same color but with smaller markers.) If amp is None, no amplification is performed.
 center_textstr, optional
Text to be placed at the very center of the polar plot (sometimes useful to use as a title).
 class pygsti.report.workspaceplots.ProjectionsBoxPlot(ws, projections, projection_basis, color_min=None, color_max=None, box_labels=False, colorbar=None, prec='compacthp', scale=1.0, eb_matrix=None, title=None)
Bases:
pygsti.report.workspace.WorkspacePlot
Plot of matrix of (usually errorgenerator) projections
Parameters
 wsWorkspace
The containing (parent) workspace.
 projectionsndarray
A 1dimensional array of length equal to the numer of elements in the given basis (usually equal to the gate dimension). Ordering of the values is assumed to correspond to the ordering given by the routines in pygsti.tools, (e.g.
pp_matrices()
when projection_basis equals “pp”). projection_basis{‘std’, ‘gm’, ‘pp’, ‘qt’}
The basis is used to construct the error generators onto which the gate error generator is projected. Allowed values are Matrixunit (std), GellMann (gm), Pauliproduct (pp) and Qutrit (qt).
 color_minfloat, optional
Minimum value of the color scale. If None, then computed automatically from the data range.
 color_maxfloat, optional
Maximum value of the color scale. If None, then computed automatically from the data range.
 box_labelsbool, optional
Whether box labels are displayed.
 colorbarbool optional
Whether to display a color bar to the right of the box plot. If None, then a colorbar is displayed when box_labels == False.
 precint or {‘compact’,’compacthp’}, optional
Precision for box labels. Only relevant when box_labels == True. Allowed values are:
‘compact’ = round to nearest whole number using at most 3 characters
‘compacthp’ = show as much precision as possible using at most 3 characters
int >= 0 = fixed precision given by int
int < 0 = number of significant figures given by int
 scalefloat, optional
Scaling factor to adjust the size of the final figure.
 eb_matrixnumpy array, optional
An array, of the same size as projections, which gives error bars to be be displayed in the hover info.
 titlestr, optional
A title for the plot
Creates a color box plot displaying projections.
Typically projections is obtained by calling
std_errorgen_projections()
, and so holds the projections of a gate error generator onto the generators corresponding to a set of standard errors constructed from the given basis.Parameters
 projectionsndarray
A 1dimensional array of length equal to the numer of elements in the given basis (usually equal to the gate dimension). Ordering of the values is assumed to correspond to the ordering given by the routines in pygsti.tools, (e.g.
pp_matrices()
when projection_basis equals “pp”). projection_basis{‘std’, ‘gm’, ‘pp’, ‘qt’}
The basis is used to construct the error generators onto which the gate error generator is projected. Allowed values are Matrixunit (std), GellMann (gm), Pauliproduct (pp) and Qutrit (qt).
 color_min,color_maxfloat, optional
Color scale min and max values, respectivey. If None, then computed automatically from the data range.
 box_labelsbool, optional
Whether box labels are displayed.
 colorbarbool optional
Whether to display a color bar to the right of the box plot. If None, then a colorbar is displayed when box_labels == False.
 precint or {‘compact’,’compacthp’}, optional
Precision for box labels. Only relevant when box_labels == True. Allowed values are:
‘compact’ = round to nearest whole number using at most 3 characters
‘compacthp’ = show as much precision as possible using at most 3 characters
int >= 0 = fixed precision given by int
int < 0 = number of significant figures given by int
 scalefloat, optional
Scaling factor to adjust the size of the final figure.
 eb_matrixnumpy array, optional
An array, of the same size as projections, which gives error bars to be be displayed in the hover info.
 titlestr, optional
A title for the plot
 class pygsti.report.workspaceplots.ChoiEigenvalueBarPlot(ws, evals, errbars=None, scale=1.0)
Bases:
pygsti.report.workspace.WorkspacePlot
Bar plot of eigenvalues showing red bars for negative values
Parameters
 wsWorkspace
The containing (parent) workspace.
 evalsndarray
An array containing the eigenvalues to plot.
 errbarsndarray, optional
An array containing the lengths of the error bars to place on each bar of the plot.
 scalefloat, optional
Scaling factor to adjust the size of the final figure.
Creates a bar plot showing the real parts of each of the eigenvalues given. This is useful for plotting the eigenvalues of Choi matrices, since all elements are positive for a CPTP map.
Parameters
 evalsndarray
An array containing the eigenvalues to plot.
 errbarsndarray, optional
An array containing the lengths of the error bars to place on each bar of the plot.
 scalefloat, optional
Scaling factor to adjust the size of the final figure.
 class pygsti.report.workspaceplots.GramMatrixBarPlot(ws, dataset, target, maxlen=10, fixed_lists=None, scale=1.0)
Bases:
pygsti.report.workspace.WorkspacePlot
Bar plot of Gram matrix eigenvalues stacked against those of target
Parameters
 wsWorkspace
The containing (parent) workspace.
 datasetDataSet
The DataSet
 targetModel
A target model which is used for it’s mapping of SPAM labels to SPAM specifiers and for Gram matrix comparision.
 maxleninteger, optional
The maximum length string used when searching for the maximal (best) Gram matrix. It’s useful to make this at least twice the maximum length fiducial sequence.
 fixed_lists(prep_fiducials, meas_fiducials), optional
2tuple of circuit lists, specifying the preparation and measurement fiducials to use when constructing the Gram matrix, and thereby bypassing the search for such lists.
 scalefloat, optional
Scaling factor to adjust the size of the final figure.
Creates a bar plot showing eigenvalues of the Gram matrix compared to those of the a target model’s Gram matrix.
Parameters
 datasetDataSet
The DataSet
 targetModel
A target model which is used for it’s mapping of SPAM labels to SPAM specifiers and for Gram matrix comparision.
 maxleninteger, optional
The maximum length string used when searching for the maximal (best) Gram matrix. It’s useful to make this at least twice the maximum length fiducial sequence.
 fixed_lists(prep_fiducials, meas_fiducials), optional
2tuple of circuit lists, specifying the preparation and measurement fiducials to use when constructing the Gram matrix, and thereby bypassing the search for such lists.
 scalefloat, optional
Scaling factor to adjust the size of the final figure.
 class pygsti.report.workspaceplots.FitComparisonBarPlot(ws, x_names, circuits_by_x, model_by_x, dataset_by_x, objfn_builder='logl', x_label='L', np_by_x=None, scale=1.0, comm=None, wildcard=None)
Bases:
pygsti.report.workspace.WorkspacePlot
Bar plot showing the overall (aggregate) goodness of fit (along one dimension).
Parameters
 wsWorkspace
The containing (parent) workspace.
 x_nameslist
List of xvalues. Typically these are the integer maximum lengths or exponents used to index the different iterations of GST, but they can also be strings.
 circuits_by_xlist of (CircuitLists or lists of Circuits)
Specifies the set of circuits used at each xvalue.
 model_by_xlist of Models
Model corresponding to each xvalue.
 dataset_by_xDataSet or list of DataSets
The data sets to compare each model against. If a single
DataSet
is given, then it is used for all comparisons. objfn_builderObjectiveFunctionBuilder or {“logl”, “chi2”}, optional
The objective function to use, or one of the given strings to use a defaut loglikelihood or chi^2 function.
 x_labelstr, optional
A label for the ‘x’ variable which indexes the different models. This string will be the xlabel of the resulting bar plot.
 np_by_xlist of ints, optional
A list of parameter counts to use for each x. If None, then the number of nongauge parameters for each model is used.
 scalefloat, optional
Scaling factor to adjust the size of the final figure.
 commmpi4py.MPI.Comm, optional
When not None, an MPI communicator for distributing the computation across multiple processors.
 wildcardWildcardBudget
A wildcard budget to apply to the objective function (objective), which increases the goodness of fit by adjusting (by an amount measured in TVD) the probabilities produced by a model before comparing with the frequencies in dataset. Currently, this functionality is only supported for objective == “logl”.
Creates a bar plot showing the overall (aggregate) goodness of fit for one or more model estimates to corresponding data sets.
Parameters
 x_nameslist
List of xvalues. Typically these are the integer maximum lengths or exponents used to index the different iterations of GST, but they can also be strings.
 circuits_by_xlist of (CircuitLists or lists of Circuits)
Specifies the set of circuits used at each xvalue.
 model_by_xlist of Models
Model corresponding to each xvalue.
 dataset_by_xDataSet or list of DataSets
The data sets to compare each model against. If a single
DataSet
is given, then it is used for all comparisons. objfn_builderObjectiveFunctionBuilder or {“logl”, “chi2”}, optional
The objective function to use, or one of the given strings to use a defaut loglikelihood or chi^2 function.
 x_labelstr, optional
A label for the ‘x’ variable which indexes the different models. This string will be the xlabel of the resulting bar plot.
 np_by_xlist of ints, optional
A list of parameter counts to use for each x. If None, then the number of nongauge parameters for each model is used.
 scalefloat, optional
Scaling factor to adjust the size of the final figure.
 commmpi4py.MPI.Comm, optional
When not None, an MPI communicator for distributing the computation across multiple processors.
 wildcardWildcardBudget
A wildcard budget to apply to the objective function (objective), which increases the goodness of fit by adjusting (by an amount measured in TVD) the probabilities produced by a model before comparing with the frequencies in dataset. Currently, this functionality is only supported for objective == “logl”.
 class pygsti.report.workspaceplots.FitComparisonBoxPlot(ws, xs, ys, circuits_by_y_then_x, model_by_y_then_x, dataset_by_y_then_x, objfn_builder='logl', x_label=None, y_label=None, scale=1.0, comm=None, wildcard=None)
Bases:
pygsti.report.workspace.WorkspacePlot
Box plot showing the overall (aggregate) goodness of fit (along 2 dimensions).
Parameters
 wsWorkspace
The containing (parent) workspace.
 xslist
List of Xvalues (converted to strings).
 yslist
List of Yvalues (converted to strings).
 circuits_by_y_then_xlist of lists of PlaquetteGridCircuitStructure objects
Specifies the circuits used at each Y and X value, indexed as circuits_by_y_then_x[iY][iX], where iX and iY are X and Y indices, respectively.
 model_by_y_then_xlist of lists of Models
Model corresponding to each X and Y value.
 dataset_by_y_then_xlist of lists of DataSets
DataSet corresponding to each X and Y value.
 objfn_builderObjectiveFunctionBuilder or {“logl”, “chi2”}, optional
The objective function to use, or one of the given strings to use a defaut loglikelihood or chi^2 function.
 x_labelstr, optional
Label for the ‘X’ variable which indexes different models. This string will be the xlabel of the resulting box plot.
 y_labelstr, optional
Label for the ‘Y’ variable which indexes different models. This string will be the ylabel of the resulting box plot.
 scalefloat, optional
Scaling factor to adjust the size of the final figure.
 commmpi4py.MPI.Comm, optional
When not None, an MPI communicator for distributing the computation across multiple processors.
 wildcardWildcardBudget
A wildcard budget to apply to the objective function (objective), which increases the goodness of fit by adjusting (by an amount measured in TVD) the probabilities produced by a model before comparing with the frequencies in dataset. Currently, this functionality is only supported for objective == “logl”.
Creates a box plot showing the overall (aggregate) goodness of fit for one or more model estimates to their respective data sets.
Parameters
 xs, yslist
List of Xvalues and Yvalues (converted to strings).
 circuits_by_y_then_xlist of lists of PlaquetteGridCircuitStructure objects
Specifies the circuits used at each Y and X value, indexed as circuits_by_y_then_x[iY][iX], where iX and iY are X and Y indices, respectively.
 model_by_y_then_xlist of lists of Models
Model corresponding to each X and Y value.
 dataset_by_y_then_xlist of lists of DataSets
DataSet corresponding to each X and Y value.
 objfn_builderObjectiveFunctionBuilder or {“logl”, “chi2”}, optional
The objective function to use, or one of the given strings to use a defaut loglikelihood or chi^2 function.
 x_label, y_labelstr, optional
Labels for the ‘X’ and ‘Y’ variables which index the different gate sets. These strings will be the x and ylabel of the resulting box plot.
 scalefloat, optional
Scaling factor to adjust the size of the final figure.
 commmpi4py.MPI.Comm, optional
When not None, an MPI communicator for distributing the computation across multiple processors.
 wildcardWildcardBudget
A wildcard budget to apply to the objective function (objective), which increases the goodness of fit by adjusting (by an amount measured in TVD) the probabilities produced by a model before comparing with the frequencies in dataset. Currently, this functionality is only supported for objective == “logl”.
 class pygsti.report.workspaceplots.DatasetComparisonSummaryPlot(ws, dslabels, dsc_dict, scale=1.0)
Bases:
pygsti.report.workspace.WorkspacePlot
A grid of grayscale boxes comparing data sets pairwise.
This class creates a plot showing the total 2*deltaLogL values for each pair of
DataSet
out of some number of total DataSets.Background: For every pair of data sets, the likelihood is computed for two different models: 1) the model in which a single set of probabilities (one per gate sequence, obtained by the combined outcome frequencies) generates both data sets, and 2) the model in which each data is generated from different sets of probabilities. Twice the ratio of these loglikelihoods can be compared to the value that is expected when model 1) is valid. This plot shows the difference between the expected and actual twiceloglikelihood ratio in units of standard deviations. Zero or negative values indicate the data sets appear to be generated by the same underlying probabilities. Large positive values indicate the data sets appear to be generated by different underlying probabilities.
Parameters
 wsWorkspace
The containing (parent) workspace.
 dslabelslist
A list of data set labels, specifying the ordering and the number of data sets.
 dsc_dictdict
A dictionary of DataComparator objects whose keys are 2tuples of integers such that the value associated with (i,j) is a DataComparator object that compares the ith and jth data sets (as indexed by dslabels.
 scalefloat, optional
Scaling factor to adjust the size of the final figure.
Creates a plot showing the total 2*deltaLogL values for each pair of DataSets out of some number of total DataSets.
Background: For every pair of data sets, the likelihood is computed for two different models: 1) the model in which a single set of probabilities (one per gate sequence, obtained by the combined outcome frequencies) generates both data sets, and 2) the model in which each data is generated from different sets of probabilities. Twice the ratio of these loglikelihoods can be compared to the value that is expected when model 1) is valid. This plot shows the difference between the expected and actual twiceloglikelihood ratio in units of standard deviations. Zero or negative values indicate the data sets appear to be generated by the same underlying probabilities. Large positive values indicate the data sets appear to be generated by different underlying probabilities.
Parameters
 dslabelslist
A list of data set labels, specifying the ordering and the number of data sets.
 dsc_dictdict
A dictionary of DataComparator objects whose keys are 2tuples of integers such that the value associated with (i,j) is a DataComparator object that compares the ith and jth data sets (as indexed by dslabels.
 class pygsti.report.workspaceplots.DatasetComparisonHistogramPlot(ws, dsc, nbins=50, frequency=True, log=True, display='pvalue', scale=1.0)
Bases:
pygsti.report.workspace.WorkspacePlot
Histogram of pvalues comparing two data sets
Parameters
 wsWorkspace
The containing (parent) workspace.
 dscDataComparator
The data set comparator, which holds and compares the data.
 nbinsint, optional
Bins in the histogram.
 frequencybool, optional
Whether the frequencies (instead of the counts) are used. TODO: more detail.
 logbool, optional
Whether to set a logscale on the xaxis or not.
 display{‘pvalue’, ‘llr’}, optional
What quantity to display (in histogram).
 scalefloat, optional
Scaling factor to adjust the size of the final figure.
Create a new WorkspaceTable. Usually not called directly.
Parameters
 wsWorkspace
The workspace containing the new object.
 fnfunction
A tablecreating function.
 argsvarious
The arguments to fn.
 class pygsti.report.workspaceplots.WildcardSingleScaleBarPlot(ws, budget, scale=1.0, reference_name='Reference Value')
Bases:
pygsti.report.workspace.WorkspacePlot
Stacked bar plot showing pergate reference values and wildcard budgets.
Typically these reference values are a gate metric comparable to wildcard budget such as diamond distance, and the bars show the relative modeled vs. unmodeled error.
Parameters
 wsWorkspace
The containing (parent) workspace.
 budgetPrimitiveOpsSingleScaleWildcardBudget
Wildcard budget to be plotted.
Create a new WorkspaceTable. Usually not called directly.
Parameters
 wsWorkspace
The workspace containing the new object.
 fnfunction
A tablecreating function.
 argsvarious
The arguments to fn.
 class pygsti.report.workspaceplots.RandomizedBenchmarkingPlot(ws, rb_r, fitkey=None, decay=True, success_probabilities=True, ylim=None, xlim=None, showpts=True, legend=True, title=None, scale=1.0)
Bases:
pygsti.report.workspace.WorkspacePlot
Plot of RB Decay curve
Parameters
 wsWorkspace
The containing (parent) workspace.
 rb_rRandomizedBenchmarkingResults
The RB results object containing all the relevant RB data.
 fitkeydict key, optional
The key of the self.fits dictionary to plot the fit for. If None, will look for a ‘full’ key (the key for a full fit to A + Bp^m if the standard analysis functions are used) and plot this if possible. It otherwise checks that there is only one key in the dict and defaults to this. If there are multiple keys and none of them are ‘full’, fitkey must be specified when decay is True.
 decaybool, optional
Whether to plot a fit, or just the data.
 success_probabilitiesbool, optional
Whether to plot the success probabilities distribution, as a “box & whisker” plot.
 ylimtuple, optional
The y limits for the figure.
 xlimtuple, optional
The x limits for the figure.
 showptsbool, optional
When success_probabilities == True, whether individual points should be shown along with a “box & whisker”.
 legendbool, optional
Whether to show a legend.
 titlestr, optional
A title to put on the figure.
 scalefloat, optional
Scaling factor to adjust the size of the final figure.
Plot RB decay curve, as a function of sequence length. Optionally includes a fitted exponential decay.
Parameters
 rb_rRandomizedBenchmarkingResults
The RB results object containing all the relevant RB data.
 fitkeydict key, optional
The key of the self.fits dictionary to plot the fit for. If None, will look for a ‘full’ key (the key for a full fit to A + Bp^m if the standard analysis functions are used) and plot this if possible. It otherwise checks that there is only one key in the dict and defaults to this. If there are multiple keys and none of them are ‘full’, fitkey must be specified when decay is True.
 decaybool, optional
Whether to plot a fit, or just the data.
 success_probabilitiesbool, optional
Whether to plot the success probabilities distribution, as a “box & whisker” plot.
 ylim, xlimtuple, optional
The x and y limits for the figure.
 showptsbool, optional
When success_probabilities == True, whether individual points should be shown along with a “box & whisker”.
 legendbool, optional
Whether to show a legend.
 titlestr, optional
A title to put on the figure.
Returns
None