Fix typos in math and histfactory tutorials and others

guitargeek · guitargeek · commit df2aaa7e2909 · 2023-11-14T23:28:41.000+01:00
diff --git a/tutorials/hist/hlHisto3.C b/tutorials/hist/hlHisto3.C
@@ -2,8 +2,8 @@
 /// \ingroup tutorial_hist
 ///
 /// This tutorial demonstrates how the highlight mechanism can be used on a ntuple.
-/// The ntuple in `hsimple.root` is drawn with three differents selection. Moving
-/// the mouse ove the two 1D representation display the on 2D plot the events
+/// The ntuple in `hsimple.root` is drawn with three different selections. Moving
+/// the mouse over the two 1D representation display the on 2D plot the events
 /// contributing to the highlighted bin.
 ///
 /// \macro_code
diff --git a/tutorials/histfactory/ModifyInterpolation.C b/tutorials/histfactory/ModifyInterpolation.C
@@ -63,7 +63,7 @@ void CheckInterpolation(RooWorkspace* ws);
 
 // Codes for interpolation
 // code = 0: piece-wise linear
-// code = 1: pice-wise log
+// code = 1: piece-wise log
 // code = 2: parabolic interp with linear extrap
 // code = 3: parabolic version of log-normal
 
diff --git a/tutorials/histfactory/example_DataDriven_controlRegion.xml b/tutorials/histfactory/example_DataDriven_controlRegion.xml
@@ -1,5 +1,5 @@
 <!--
-    Single channel configuration exampple.
+    Single channel configuration example.
     The top level configuration XML is example.xml
 
     NormalizedByTheory should be "True" (not "TRUE" or "true") for all non-data-driven backgrounds.
diff --git a/tutorials/histfactory/example_DataDriven_signalRegion.xml b/tutorials/histfactory/example_DataDriven_signalRegion.xml
@@ -1,5 +1,5 @@
 <!--
-    Single channel configuration exampple.
+    Single channel configuration example.
     The top level configuration XML is example.xml
 
     NormalizedByTheory should be "True" (not "TRUE" or "true") for all non-data-driven backgrounds.
diff --git a/tutorials/histfactory/example_Expression_channel.xml b/tutorials/histfactory/example_Expression_channel.xml
@@ -1,5 +1,5 @@
 <!--
-    Single channel configuration exampple.
+    Single channel configuration example.
     The top level configuration XML is example.xml
 
     NormalizedByTheory should be "True" (not "TRUE" or "true") for all non-data-driven backgrounds.
diff --git a/tutorials/histfactory/example_ShapeSys_channel.xml b/tutorials/histfactory/example_ShapeSys_channel.xml
@@ -1,5 +1,5 @@
 <!--
-    Single channel configuration exampple.
+    Single channel configuration example.
     The top level configuration XML is example.xml
 
     NormalizedByTheory should be "True" (not "TRUE" or "true") for all non-data-driven backgrounds.
diff --git a/tutorials/histfactory/example_channel.xml b/tutorials/histfactory/example_channel.xml
@@ -1,5 +1,5 @@
 <!--
-    Single channel configuration exampple.
+    Single channel configuration example.
     The top level configuration XML is example.xml
 
     NormalizedByTheory should be "True" (not "TRUE" or "true") for all non-data-driven backgrounds.
diff --git a/tutorials/histfactory/makeQuickModel.py b/tutorials/histfactory/makeQuickModel.py
@@ -137,7 +137,7 @@ def MakeSimpleMeasurement( signal_val, background_val, data_val, signal_uncertai
 
     # Create a channel and set
     # the measured value of data 
-    # (no extenal hist necessar for cut-and-count)
+    # (no external hist necessary for cut-and-count)
     chan = ROOT.RooStats.HistFactory.Channel( "channel" )
     chan.SetData( data_val )
 
@@ -147,7 +147,7 @@ def MakeSimpleMeasurement( signal_val, background_val, data_val, signal_uncertai
     signal.SetValue( signal_val )
     #signal.SetValue( 10 )
 
-    # Add the parmaeter of interest and a systematic
+    # Add the parameter of interest and a systematic
     # Try to make intelligent choice of upper bound
     import math
     upper_bound = 3*math.ceil( (data_val - background_val) / signal_val )
diff --git a/tutorials/math/GammaFun.C b/tutorials/math/GammaFun.C
@@ -51,7 +51,7 @@ void GammaFun() {
 
    //setting graph
    // draw axis first (use TH1 to draw the frame)
-   TH1F * h = new TH1F("htmp","",500,-2,5);
+   TH1F * h = new TH1F("hTemp","",500,-2,5);
    h->SetMinimum(-20);
    h->SetMaximum(20);
    h->GetXaxis()->SetTitleSize(0.06);
diff --git a/tutorials/math/goftest.C b/tutorials/math/goftest.C
@@ -85,7 +85,7 @@ void goftest() {
    Double_t pvalueAD_2 = (*goftest_1)(); // p-value and Anderson - Darling Test are the default choices
    assert(pvalueAD_1 == pvalueAD_2);
 
-   // Rebuild the test using the default 1-sample construtor 
+   // Rebuild the test using the default 1-sample constructor 
    delete goftest_1;
    goftest_1 = new ROOT::Math::GoFTest(nEvents1, sample1 ); // User must then input a distribution type option
    goftest_1->SetDistribution(ROOT::Math::GoFTest::kLogNormal);
@@ -184,7 +184,7 @@ void goftest() {
    assert(pvalueKS_1 == pvalueKS_2);
 
 #ifdef TEST_ERROR_MESSAGE
-   /* Valid but incorrect calls for the 1-sample methods of the 2-samples constucted goftest_2 */
+   /* Valid but incorrect calls for the 1-sample methods of the 2-samples constructed goftest_2 */
    A2 = (*goftest_2)(ROOT::Math::GoFTest::kAD, "t");     // Issues error message
    pvalueKS = (*goftest_2)(ROOT::Math::GoFTest::kKS);    // Issues error message
    assert(A2 == pvalueKS);
diff --git a/tutorials/math/mathcoreGenVector.C b/tutorials/math/mathcoreGenVector.C
@@ -748,7 +748,7 @@ int testRotation() {
    std::cout << "Test Plane3D :                  ";
    ok = 0;
 
-   // test transfrom a 3D plane
+   // test transform a 3D plane
 
    XYZPoint p1(1,2,3);
    XYZPoint p2(-2,-1,4);
diff --git a/tutorials/math/mathmoreIntegration.C b/tutorials/math/mathmoreIntegration.C
@@ -144,7 +144,7 @@ void  DrawCumulative(double x1, double x2, int n = 100){
    }
 
    TH1D *cum10 = new TH1D("cum10", "", n, x1, x2); //difference between  1 and exact
-   TH1D *cum20 = new TH1D("cum23", "", n, x1, x2); //difference between 2 and excact
+   TH1D *cum20 = new TH1D("cum23", "", n, x1, x2); //difference between 2 and exact
    for (int i = 1; i <= n; ++i) {
       double delta  =  cum1->GetBinContent(i) - cum0->GetBinContent(i);
       double delta2 =  cum2->GetBinContent(i) - cum0->GetBinContent(i);
diff --git a/tutorials/math/multidimSampling.C b/tutorials/math/multidimSampling.C
@@ -34,7 +34,7 @@
 
 bool debug = false;
 
-// Define the GausND strcture
+// Define the GausND structure
 struct GausND {
 
    TVectorD X;
diff --git a/tutorials/math/multivarGaus.C b/tutorials/math/multivarGaus.C
@@ -26,7 +26,7 @@ void multivarGaus() {
   TH2F* hYZ = new TH2F("hYZ", "hYZ;y;z;Counts", 100, -5, 5, 100, -5, 5);
 
   const int MAX = 10000;
-  for (int evnts = 0; evnts < MAX; ++evnts) {
+  for (int iEvent = 0; iEvent < MAX; ++iEvent) {
     rnd.GaussianND(dim, pars, cov, genpars);
     auto x = genpars[0];
     auto y = genpars[1];
diff --git a/tutorials/math/normalDist.py b/tutorials/math/normalDist.py
@@ -13,7 +13,7 @@
 from ROOT import TF1, TCanvas, TSystem, TAxis, TLegend
 from ROOT import kRed, kGreen, kBlue
 
-# Create the one dimentional functions for normal distribution.
+# Create the one dimensional functions for normal distribution.
 pdfunc  = TF1("pdf","ROOT::Math::normal_pdf(x, [0],[1])", -5, 5)
 cdfunc  = TF1("cdf","ROOT::Math::normal_cdf(x, [0],[1])", -5, 5)
 ccdfunc = TF1("cdf_c","ROOT::Math::normal_cdf_c(x, [0])", -5, 5)
diff --git a/tutorials/math/principal.C b/tutorials/math/principal.C
@@ -39,7 +39,7 @@ void principal(Int_t n=10, Int_t m=10000)
    TPrincipal* principal = new TPrincipal(n,"ND");
 
    // Use a pseudo-random number generator
-   TRandom* randumNum = new TRandom;
+   TRandom* randomNum = new TRandom;
 
    // Make the m data-points
    // Make a variable to hold our data
@@ -50,9 +50,9 @@ void principal(Int_t n=10, Int_t m=10000)
       // First we create the un-correlated, random variables, according
       // to one of three distributions
       for (Int_t j = 0; j < n - c; j++) {
-         if (j % 3 == 0)      data[j] = randumNum->Gaus(5,1);
-         else if (j % 3 == 1) data[j] = randumNum->Poisson(8);
-         else                 data[j] = randumNum->Exp(2);
+         if (j % 3 == 0)      data[j] = randomNum->Gaus(5,1);
+         else if (j % 3 == 1) data[j] = randomNum->Poisson(8);
+         else                 data[j] = randomNum->Exp(2);
       }
 
       // Then we create the correlated variables
@@ -77,7 +77,7 @@ void principal(Int_t n=10, Int_t m=10000)
    // Test the PCA
    principal->Test();
 
-   // Make some histograms of the orginal, principal, residue, etc data
+   // Make some histograms of the original, principal, residue, etc data
    principal->MakeHistograms();
 
    // Make two functions to map between feature and pattern space
diff --git a/tutorials/math/principal.py b/tutorials/math/principal.py
@@ -39,7 +39,7 @@
 principal = TPrincipal(n, "ND")
 
 # Use a pseudo-random number generator
-randumNum = gRandom
+randomNum = gRandom
 
 # Make the m data-points
 # Make a variable to hold our data
@@ -50,11 +50,11 @@
     # to one of three distributions
     for j in range(n - c):
         if j % 3 == 0:
-            data.push_back(randumNum.Gaus(5, 1))
+            data.push_back(randomNum.Gaus(5, 1))
         elif j % 3 == 1:
-            data.push_back(randumNum.Poisson(8))
+            data.push_back(randomNum.Poisson(8))
         else:
-            data.push_back(randumNum.Exp(2))
+            data.push_back(randomNum.Exp(2))
 
     # Then we create the correlated variables
     for j in range(c):
@@ -75,7 +75,7 @@
 # Test the PCA
 principal.Test()
 
-# Make some histograms of the orginal, principal, residue, etc data
+# Make some histograms of the original, principal, residue, etc data
 principal.MakeHistograms()
 
 # Make two functions to map between feature and pattern space
diff --git a/tutorials/math/testrandom.C b/tutorials/math/testrandom.C
@@ -572,7 +572,7 @@ void testAll() {
   }
   printf(" %8.3f\n",sw.CpuTime()*cpn);
 
-  // Poisson (nned to initialize before with Poisson mu value)
+  // Poisson (need to initialize before with Poisson mu value)
 
   unr0.InitPoisson(10);
   unr1.InitPoisson(10);
diff --git a/tutorials/tmva/TMVACrossValidation.C b/tutorials/tmva/TMVACrossValidation.C
@@ -233,7 +233,7 @@ int TMVACrossValidation(bool useRandomSplitting = false)
    // --------------------------------------------------------------------------
 
    //
-   // Process some output programatically, printing the ROC score for each
+   // Process some output programmatically, printing the ROC score for each
    // booked method.
    //
    size_t iMethod = 0;
diff --git a/tutorials/tmva/TMVA_SOFIE_Models.py b/tutorials/tmva/TMVA_SOFIE_Models.py
@@ -19,7 +19,7 @@
 ROOT.TMVA.PyMethodBase.PyInitialize()
 
 
-## generate and train Keras models with different architetures
+## generate and train Keras models with different architectures
 
 import numpy as np
 from tensorflow.keras.models import Sequential
@@ -200,4 +200,4 @@ def MakeROCCurve(hs, hb) :
 curve3.SetLineColor(ROOT.kGreen)
 curve3.Draw("C")
 
-c2.Draw()
+c2.Draw()
diff --git a/tutorials/tmva/createData.C b/tutorials/tmva/createData.C
diff --git a/tutorials/tmva/keras/GenerateModel.py b/tutorials/tmva/keras/GenerateModel.py
diff --git a/tutorials/tmva/tmva102_Testing.py b/tutorials/tmva/tmva102_Testing.py
diff --git a/tutorials/tree/parallelcoord.C b/tutorials/tree/parallelcoord.C
diff --git a/tutorials/vecops/vo002_VectorCalculations.C b/tutorials/vecops/vo002_VectorCalculations.C
diff --git a/tutorials/vecops/vo003_LogicalOperations.C b/tutorials/vecops/vo003_LogicalOperations.C
