Checkpoint changes to tvd plot

Checkpoint the implementation of a new TVD plot for reports.

Author: Corey Ostrove

Diff for: pygsti/report/section/summary.py

@@ -33,6 +33,15 @@ def final_model_fit_histogram(workspace, switchboard=None, linlog_percentile=5,
             linlg_pcntle=linlog_percentile / 100,
             typ='histogram', comm=comm, bgcolor=bgcolor
         )
+    
+    @_Section.figure_factory()
+    def final_tvd_histogram(workspace, switchboard=None, comm=None, bgcolor='white',
+                                  **kwargs):
+        return workspace.ColorBoxPlot(
+            'tvd', switchboard.circuits_final,
+            switchboard.modvi_ds, switchboard.mdl_current_modvi,
+            typ='histogram', comm=comm, bgcolor=bgcolor
+        )
 
     @_Section.figure_factory()
     def final_gates_vs_target_table_insummary(workspace, switchboard=None, confidence_level=None, ci_brevity=1,

Diff for: pygsti/report/templates/standard_html_report/tabs/Summary.html

@@ -16,6 +16,11 @@ <h1>Summary</h1>
     {{ final_model_fit_histogram|render }}
 </figure>
 
+<figure id="final_tvd_histogram">
+	<figcaption><span class="captiontitle">Histogram of per-circuit TVD.</span> <span class="captiondetail">This figure is about goodness-of-fit.  When the estimate doesn't fit the data perfectly, we can quantify how well it fails to predict each individual circuit in the dataset, using the excess loglikelihood (<span class="math">-2\log\mathrm{Pr}(\mathrm{data}|\mathrm{gateset})</span>) above and beyond the minimum value (<span class="math">-2 \log \mathrm{Pr}(\mathrm{data}|\mathrm{observed\ frequencies})</span>).  This plot shows a histogram of the those values for all the circuits in the dataset.  Ideally, they should have the <span class="math">\chi^2</span> distribution shown by the solid line.  Red indicates data that are inconsistent with the model at the 0.95 confidence level, as shown in more detail in the Model Violation tab.</span> </figcaption>
+    {{ final_tvd_histogram|render }}
+</figure>
+
 <figure id="final_gates_vs_target_table_insummary" class='tbl'>
 	<figcaption><span class="captiontitle">Comparison of estimated gates to targets.</span> <span class="captiondetail">This table is about gate error metrics (fidelity). The metrics in this table compare the estimated gates to their ideal counterparts, and can generally be interpreted as some kind of error rate (per gate use).  Entanglement (process) fidelity and 1/2-diamond norm are the best known of these; they are the same for purely stochastic errors, but coherent errors contribute much more to diamond norm.  1/2-trace-distance is a proxy for diamond norm that doesn't require cvxPy to be installed.  The Eigenvalue metrics are gauge-invariant versions of fidelity and diamond-norm that only depend on the gate itself (not its relationship to other gates).  Hovering the pointer over a heading will pop up a description.</span></figcaption>
     {{ final_gates_vs_target_table_insummary|render }}