Example bayesian workflow -- small fixes

examples/xspec/bayesian-workflow/plot_bxa.py

@@ -1528,6 +1528,7 @@ def _plot_posterior_statistic(self,solvers,
         # loop over the models and create a predictive plot for each
         if type(axs) is not np.ndarray:
             axs = (axs,)
+        self.posterior_flux_unc = [] # define for first use or clear for new use
         for ii,s in enumerate(solvers):
             # re-activate model and point solver to new instance
             if models is not None:
@@ -1536,7 +1537,6 @@ def _plot_posterior_statistic(self,solvers,
             else:
                 mname = None
             if stat == 'flux':
-                self.posterior_flux_unc = [] # define for first use or clear for new use
                 # plot the flux distributions based on the posteriors
                 self._posterior_flux_single_model(axs[ii],s,
                                                   npost=nsample,
@@ -1595,7 +1595,7 @@ def _posterior_flux_single_model(self,ax,solver,
         for row in solver.posterior[:npost]:
             bxa.solver.set_parameters(values=row,transformations=solver.transformations)
             xspec.AllModels.calcFlux(frange)
-            d += [xspec.AllData(1).flux[0]*1e12] # in units of 10^12 erg cm^-2 s^-1
+            d += [xspec.AllData(1).flux[0]*1e12] # in units of 10^-12 erg cm^-2 s^-1
         # plot the data
         c,b = np.histogram(d,bins=np.minimum(int(len(d)/10),20),
                            density=True)
@@ -1616,13 +1616,13 @@ def _posterior_flux_single_model(self,ax,solver,
         mname = solver.transformations[1]['model'].expression
         if printq:
             print(f'For model {mname}:')
-            print(f'Mean={np.mean(d):.3f} (+){q_high:.3f} (-){q_low:.3f} (10^12 erg cm^-2 s^-1)')
+            print(f'Mean={np.mean(d):.3f} (+){q_high:.3f} (-){q_low:.3f} (10^-12 erg cm^-2 s^-1)')
             print(f'With uncertainties estimated for +/- {q*100:.1f}%\n')
         self.posterior_flux_unc += [[mname,fluxrange,np.mean(d),q_high,q_low]]
         # plot details
         lblkw   = dict( (('fontsize',20),('fontweight','bold')) )
         rngstr  = f'Flux {fluxrange[0]:.1f}-{fluxrange[1]:.1f} keV'
-        unitstr = r' ($10^{12}$ erg cm$^{-2}$ s$^{-1}$)'
+        unitstr = r' ($10^{-12}$ erg cm$^{-2}$ s$^{-1}$)'
         ax.set_xlabel(rngstr+unitstr,**lblkw)
         ax.set_ylabel('Probability Density',**lblkw)
         ax.set_ylim(yl)
@@ -2001,7 +2001,7 @@ def _plot_bayes_factor_test(self,
                 return
             if mnames is None:
                 print('If loading existing data, please specify the model names')
-                return
+                return (None,None)
             m1name,m2name = mnames
             logz1, logz2 = [], []
             fitd1, fitd2 = [], []

examples/xspec/bayesian-workflow/plot_xspec.py

@@ -106,7 +106,7 @@ def _get_xspec_data(self,model=True,plottype='folded',
                                 maxBins=rebinbnum,
                                 groupNum=-1)
         else:
-            xspec.Plot.setRebin(minSig=0,maxBins=-1,groupNum=-1)
+            xspec.Plot.setRebin(minSig=0,maxBins=1,groupNum=-1)
         if plottype == 'folded':
             plttype = 'data'
         elif plottype == 'counts':