[RF] Avoid conversion from Python sets to RooArgSet

This pattern should not be used, because it leaves the RooArgSet in a
non-deterministic ordering, depending on the implementation of Python.

Author: guitargeek

tutorials/roofit/roofit/rf101_basics.py

@@ -40,7 +40,7 @@
 # Generate events
 # -----------------------------
 # Generate a dataset of 1000 events in x from gauss
-data = gauss.generate({x}, 10000)  # ROOT.RooDataSet
+data = gauss.generate(x, 10000)  # ROOT.RooDataSet
 
 # Make a second plot frame in x and draw both the
 # data and the pdf in the frame

tutorials/roofit/roofit/rf102_dataimport.py

@@ -104,7 +104,7 @@ def makeTTree(trnd):
 # and RRV y defines a range [-10,10] this means that the ROOT.RooDataSet
 # below will have less entries than the ROOT.TTree 'tree'
 
-ds = ROOT.RooDataSet("ds", "ds", {x, y}, Import=tree)
+ds = ROOT.RooDataSet("ds", "ds", [x, y], Import=tree)
 
 # Use ascii import/export for datasets
 # ------------------------------------------------------------------------------------

tutorials/roofit/roofit/rf103_interprfuncs.py

@@ -31,7 +31,7 @@
 # ---------------------------------------------------------------
 
 # Generate a toy dataset from the interpreted pdf
-data = genpdf.generate({x}, 10000)
+data = genpdf.generate(x, 10000)
 
 # Fit the interpreted pdf to the generated data
 genpdf.fitTo(data, PrintLevel=-1)
@@ -64,7 +64,7 @@
 # Construct a separate gaussian g1(x,10,3) to generate a toy Gaussian
 # dataset with mean 10 and width 3
 g1 = ROOT.RooGaussian("g1", "g1", x, 10, 3)
-data2 = g1.generate({x}, 1000)
+data2 = g1.generate(x, 1000)
 
 # Fit and plot tailored standard pdf
 # -------------------------------------------------------------------

tutorials/roofit/roofit/rf104_classfactory.py

@@ -67,7 +67,7 @@
 
 # Generate toy data from pdf and plot data and pdf on frame
 frame1 = y.frame(Title="Compiled class MyPdfV3")
-data = pdf.generate({y}, 1000)
+data = pdf.generate(y, 1000)
 pdf.fitTo(data, PrintLevel=-1)
 data.plotOn(frame1)
 pdf.plotOn(frame1)
@@ -87,7 +87,7 @@
 genpdf = ROOT.RooClassFactory.makePdfInstance("GenPdf", "(1+0.1*fabs(x)+sin(sqrt(fabs(x*alpha+0.1))))", [x, alpha])
 
 # Generate a toy dataset from the interpreted pdf
-data2 = genpdf.generate({x}, 50000)
+data2 = genpdf.generate(x, 50000)
 
 # Fit the interpreted pdf to the generated data
 genpdf.fitTo(data2, PrintLevel=-1)

tutorials/roofit/roofit/rf105_funcbinding.py

@@ -41,7 +41,7 @@
 beta.Print()
 
 # Generate some events and fit
-data = beta.generate({x2}, 10000)
+data = beta.generate(x2, 10000)
 beta.fitTo(data, PrintLevel=-1)
 
 # Plot data and pdf on frame

tutorials/roofit/roofit/rf106_plotdecoration.py

@@ -23,7 +23,7 @@
 gauss = ROOT.RooGaussian("gauss", "gauss", x, mean, sigma)
 
 # Generate a sample of 1000 events with sigma=3
-data = gauss.generate({x}, 1000)
+data = gauss.generate(x, 1000)
 
 # Fit pdf to data
 gauss.fitTo(data, PrintLevel=-1)

tutorials/roofit/roofit/rf107_plotstyles.py

@@ -25,7 +25,7 @@
 gauss = ROOT.RooGaussian("gauss", "gauss", x, mean, sigma)
 
 # Generate a sample of 100 events with sigma=3
-data = gauss.generate({x}, 100)
+data = gauss.generate(x, 100)
 
 # Fit pdf to data
 gauss.fitTo(data, PrintLevel=-1)

tutorials/roofit/roofit/rf108_plotbinning.py

@@ -38,7 +38,7 @@
 # --------------------------------------------
 
 # Sample 2000 events in (dt,mixState,tagFlav) from bmix
-data = bmix.generate({dt, mixState, tagFlav}, 2000)
+data = bmix.generate([dt, mixState, tagFlav], 2000)
 
 # Show dt distribution with custom binning
 # -------------------------------------------------------------------------------

tutorials/roofit/roofit/rf109_chi2residpull.py

@@ -26,7 +26,7 @@
 gauss = ROOT.RooGaussian("gauss", "gauss", x, mean, sigma)
 
 # Generate a sample of 1000 events with sigma=3
-data = gauss.generate({x}, 10000)
+data = gauss.generate(x, 10000)
 
 # Change sigma to 3.15
 sigma.setVal(3.15)

tutorials/roofit/roofit/rf110_normintegration.py

@@ -34,7 +34,7 @@
 
 # Create object representing integral over gx
 # which is used to calculate  gx_Norm[x] == gx / gx_Int[x]
-igx = gx.createIntegral({x})
+igx = gx.createIntegral(x)
 print("gx_Int[x] = ", igx.getVal())
 
 # Integrate normalized pdf over subrange
@@ -46,7 +46,7 @@
 # Create an integral of gx_Norm[x] over x in range "signal"
 # ROOT.This is the fraction of of pdf gx_Norm[x] which is in the
 # range named "signal"
-xset = {x}
+xset = [x]
 igx_sig = gx.createIntegral(xset, NormSet=xset, Range="signal")
 print("gx_Int[x|signal]_Norm[x] = ", igx_sig.getVal())
 
@@ -55,7 +55,7 @@
 
 # Create the cumulative distribution function of gx
 # i.e. calculate Int[-10,x] gx(x') dx'
-gx_cdf = gx.createCdf({x})
+gx_cdf = gx.createCdf(x)
 
 # Plot cdf of gx versus x
 frame = x.frame(Title="cdf of Gaussian pdf")