[RF] Avoid confusing plotOn(Slice(sample), ProjWData(same)) pattern

In the documentation of the RooSimultaneous, it says about the
`ProjWData()` argument:

> For observables present in given dataset projection of PDF is achieved
  by constructing an average over all observable values in given set.

And about `Slice()`, it says:

> Override default projection behaviour by omitting the specified
  category observable from the projection, i.e., by not integrating over
  all states of this category.

Starting from this explanation, it is highly unintuitive that one should
do things like `simPdf.plotOn(Slice(sample, "state"), ProjWData(same))` when
plotting the pdf of state `"state"` from a RooSimultaneous.

I think that instead, we should promote easy patterns in the tutorials.

That is, if you want to plot a slice pdf from a RooSimultaneous, you are
retreiving it with `getPdf()` and then plot it. The result is the same.

Author: guitargeek

roofit/roofitcore/test/stressRooFit_tests.h

@@ -2972,22 +2972,21 @@ class TestBasic501 : public RooUnitTest {
       RooPlot *frame1 = x.frame(Bins(30), Title("Physics sample"));
 
       // Plot all data tagged as physics sample
-      combData.plotOn(frame1, Cut("sample==sample::physics"));
+      std::unique_ptr<RooAbsData> slicedData1{combData.reduce(Cut("sample==sample::physics"))};
+      slicedData1->SetName("combData_Cut[sample==sample::physics]"); // to be consistent with reference file
+      slicedData1->plotOn(frame1);
 
       // Plot "physics" slice of simultaneous pdf.
-      // NBL You _must_ project the sample index category with data using ProjWData
-      // as a RooSimultaneous makes no prediction on the shape in the index category
-      // and can thus not be integrated
-      simPdf.plotOn(frame1, Slice(sample, "physics"), ProjWData(sample, combData));
-      simPdf.plotOn(frame1, Slice(sample, "physics"), Components("px"), ProjWData(sample, combData),
-                    LineStyle(kDashed));
-
-      // The same plot for the control sample slice
+      simPdf.getPdf("physics")->plotOn(frame1);
+      simPdf.getPdf("physics")->plotOn(frame1, Components("px"), LineStyle(kDashed));
+
+      // The same plot for the control sample slice.
       RooPlot *frame2 = x.frame(Bins(30), Title("Control sample"));
-      combData.plotOn(frame2, Cut("sample==sample::control"));
-      simPdf.plotOn(frame2, Slice(sample, "control"), ProjWData(sample, combData));
-      simPdf.plotOn(frame2, Slice(sample, "control"), Components("px_ctl"), ProjWData(sample, combData),
-                    LineStyle(kDashed));
+      std::unique_ptr<RooAbsData> slicedData2{combData.reduce(Cut("sample==sample::control"))};
+      slicedData2->SetName("combData_Cut[sample==sample::control]"); // to be consistent with reference file
+      slicedData2->plotOn(frame2);
+      simPdf.getPdf("control")->plotOn(frame2);
+      simPdf.getPdf("control")->plotOn(frame2, Components("px_ctl"), LineStyle(kDashed));
 
       regPlot(frame1, "rf501_plot1");
       regPlot(frame2, "rf501_plot2");

tutorials/roofit/roofit/rf501_simultaneouspdf.C

@@ -102,29 +102,28 @@ void rf501_simultaneouspdf()
    RooPlot *frame1 = x.frame(Title("Physics sample"));
 
    // Plot all data tagged as physics sample
-   combData.plotOn(frame1, Cut("sample==sample::physics"));
+   std::unique_ptr<RooAbsData> slicedData1{combData.reduce(Cut("sample==sample::physics"))};
+   slicedData1->plotOn(frame1);
 
    // Plot "physics" slice of simultaneous pdf.
+   simPdf.getPdf("physics")->plotOn(frame1);
+   simPdf.getPdf("physics")->plotOn(frame1, Components("px"), LineStyle(kDashed));
+
+   // The same plot for the control sample slice. We do this with a different
+   // approach, using the data slice for the projection. This approach is more
+   // general, because you can plot sums of slices by using logical or in the
+   // Cut() command.
    // NBL You _must_ project the sample index category with data using ProjWData
    // as a RooSimultaneous makes no prediction on the shape in the index category
    // and can thus not be integrated.
    // In other words: Since the PDF doesn't know the number of events in the different
    // category states, it doesn't know how much of each component it has to project out.
    // This information is read from the data.
-   simPdf.plotOn(frame1, Slice(sample, "physics"), ProjWData(sample, combData));
-   simPdf.plotOn(frame1, Slice(sample, "physics"), Components("px"), ProjWData(sample, combData), LineStyle(kDashed));
-
-   // The same plot for the control sample slice. We do this with a different
-   // approach this time, for illustration purposes. Here, we are slicing the
-   // dataset and then use the data slice for the projection, because then the
-   // RooFit::Slice() becomes unnecessary. This approach is more general,
-   // because you can plot sums of slices by using logical or in the Cut()
-   // command.
    RooPlot *frame2 = x.frame(Bins(30), Title("Control sample"));
-   std::unique_ptr<RooAbsData> slicedData{combData.reduce(Cut("sample==sample::control"))};
-   slicedData->plotOn(frame2);
-   simPdf.plotOn(frame2, ProjWData(sample, *slicedData));
-   simPdf.plotOn(frame2, Components("px_ctl"), ProjWData(sample, *slicedData), LineStyle(kDashed));
+   std::unique_ptr<RooAbsData> slicedData2{combData.reduce(Cut("sample==sample::control"))};
+   slicedData2->plotOn(frame2);
+   simPdf.plotOn(frame2, ProjWData(sample, *slicedData2));
+   simPdf.plotOn(frame2, Components("px_ctl"), ProjWData(sample, *slicedData2), LineStyle(kDashed));
 
    // The same plot for all the phase space. Here, we can just use the original
    // combined dataset.

tutorials/roofit/roofit/rf501_simultaneouspdf.py

@@ -96,28 +96,28 @@
 frame1 = x.frame(Title="Physics sample")
 
 # Plot all data tagged as physics sample
-combData.plotOn(frame1, Cut="sample==sample::physics")
+slicedData1 = combData.reduce(Cut="sample==sample::physics")
+slicedData1.plotOn(frame1)
 
 # Plot "physics" slice of simultaneous pdf.
-# NB: You *must* project the sample index category with data using ProjWData as
-# a RooSimultaneous makes no prediction on the shape in the index category and
-# can thus not be integrated. In other words: Since the PDF doesn't know the
-# number of events in the different category states, it doesn't know how much
-# of each component it has to project out. This info is read from the data.
-simPdf.plotOn(frame1, Slice=(sample, "physics"), ProjWData=(sample, combData))
-simPdf.plotOn(frame1, Slice=(sample, "physics"), Components="px", ProjWData=(sample, combData), LineStyle="--")
+simPdf.getPdf("physics").plotOn(frame1)
+simPdf.getPdf("physics").plotOn(frame1, Components="px", LineStyle="--")
 
 # The same plot for the control sample slice. We do this with a different
-# approach this time, for illustration purposes. Here, we are slicing the
-# dataset and then use the data slice for the projection, because then the
-# RooFit::Slice() becomes unnecessary. This approach is more general,
-# because you can plot sums of slices by using logical or in the Cut()
-# command.
+# approach, using the data slice for the projection. This approach is more
+# general, because you can plot sums of slices by using logical or in the
+# Cut() command.
+# NBL You _must_ project the sample index category with data using ProjWData
+# as a RooSimultaneous makes no prediction on the shape in the index category
+# and can thus not be integrated.
+# In other words: Since the PDF doesn't know the number of events in the different
+# category states, it doesn't know how much of each component it has to project out.
+# This information is read from the data.
 frame2 = x.frame(Title="Control sample")
-slicedData = combData.reduce(Cut="sample==sample::control")
-slicedData.plotOn(frame2)
-simPdf.plotOn(frame2, ProjWData=(sample, slicedData))
-simPdf.plotOn(frame2, Components="px_ctl", ProjWData=(sample, slicedData), LineStyle="--")
+slicedData2 = combData.reduce(Cut="sample==sample::control")
+slicedData2.plotOn(frame2)
+simPdf.plotOn(frame2, ProjWData=(sample, slicedData2))
+simPdf.plotOn(frame2, Components="px_ctl", ProjWData=(sample, slicedData2), LineStyle="--")
 
 # The same plot for all the phase space. Here, we can just use the original
 # combined dataset.