Fix some tutorials documentation

tutorials/analysis/dataframe/df036_missingBranches.py

@@ -1,17 +1,18 @@
-# \file
-# \ingroup tutorial_dataframe
-# \notebook -nodraw
-#
-# This example shows how to process a dataset where entries might be
-# incomplete due to one or more missing branches in one or more of the files
-# in the dataset. It shows usage of the FilterAvailable and DefaultValueFor
-# RDataFrame functionalities to act upon the missing entries.
-#
-# \macro_code
-# \macro_output
-#
-# \date September 2024
-# \author Vincenzo Eduardo Padulano (CERN)
+## \file
+## \ingroup tutorial_dataframe
+## \notebook -nodraw
+##
+## This example shows how to process a dataset where entries might be
+## incomplete due to one or more missing branches in one or more of the files
+## in the dataset. It shows usage of the FilterAvailable and DefaultValueFor
+## RDataFrame functionalities to act upon the missing entries.
+##
+## \macro_code
+## \macro_output
+##
+## \date September 2024
+## \author Vincenzo Eduardo Padulano (CERN)
+
 import array
 import os
 

tutorials/analysis/dataframe/df037_TTreeEventMatching.py

@@ -1,18 +1,19 @@
-# \file
-# \ingroup tutorial_dataframe
-# \notebook -nodraw
-#
-# This example shows processing of a TTree-based dataset with horizontal
-# concatenations (friends) and event matching (based on TTreeIndex). In case
-# the current event being processed does not match one (or more) of the friend
-# datasets, one can use the FilterAvailable and DefaultValueFor functionalities
-# to act upon the situation.
-#
-# \macro_code
-# \macro_output
-#
-# \date September 2024
-# \author Vincenzo Eduardo Padulano (CERN)
+## \file
+## \ingroup tutorial_dataframe
+## \notebook -nodraw
+##
+## This example shows processing of a TTree-based dataset with horizontal
+## concatenations (friends) and event matching (based on TTreeIndex). In case
+## the current event being processed does not match one (or more) of the friend
+## datasets, one can use the FilterAvailable and DefaultValueFor functionalities
+## to act upon the situation.
+##
+## \macro_code
+## \macro_output
+##
+## \date September 2024
+## \author Vincenzo Eduardo Padulano (CERN)
+
 import array
 import os
 

tutorials/analysis/dataframe/distrdf004_dask_lxbatch.py

@@ -1,33 +1,34 @@
-# \file
-# \ingroup tutorial_dataframe
-#
-# Configure a Dask connection to a HTCondor cluster hosted by the CERN batch
-# service. To reproduce this tutorial, run the following steps:
-#
-# 1. Login to lxplus
-# 2. Source an LCG release (minimum LCG104). See
-#    https://lcgdocs.web.cern.ch/lcgdocs/lcgreleases/introduction/ for details
-# 3. Install the `dask_lxplus` package, which provides the `CernCluster` class
-#    needed to properly connect to the CERN condor pools. See
-#    https://batchdocs.web.cern.ch/specialpayload/dask.html for instructions
-# 4. Run this tutorial
-#
-# The tutorial defines resources that each job will request to the condor
-# scheduler, then creates a Dask client that can be used by RDataFrame to
-# distribute computations.
-#
-# \macro_code
-#
-# \date September 2023
-# \author Vincenzo Eduardo Padulano CERN
+## \file
+## \ingroup tutorial_dataframe
+##
+## Configure a Dask connection to a HTCondor cluster hosted by the CERN batch
+## service. To reproduce this tutorial, run the following steps:
+##
+## 1. Login to lxplus
+## 2. Source an LCG release (minimum LCG104). See
+##    https://lcgdocs.web.cern.ch/lcgdocs/lcgreleases/introduction/ for details
+## 3. Install the `dask_lxplus` package, which provides the `CernCluster` class
+##    needed to properly connect to the CERN condor pools. See
+##    https://batchdocs.web.cern.ch/specialpayload/dask.html for instructions
+## 4. Run this tutorial
+##
+## The tutorial defines resources that each job will request to the condor
+## scheduler, then creates a Dask client that can be used by RDataFrame to
+## distribute computations.
+##
+## \macro_code
+##
+## \date September 2023
+## \author Vincenzo Eduardo Padulano CERN
+
 from datetime import datetime
 import socket
 import time
 
 from dask.distributed import Client
 from dask_lxplus import CernCluster
 
 import ROOT
 
 
 def create_connection() -> Client:

tutorials/io/experimental/rfile001_basics.C

@@ -2,12 +2,14 @@
 /// \ingroup ROOT7 tutorial_io
 /// Demonstrate the basic usage of RFile.
 ///
-/// \author Giacomo Parolini <giacomo.parolini@cern.ch>
-/// \date 2025-11-06
 /// \macro_code
 /// \macro_output
+///
 /// \warning This is part of the ROOT 7 prototype! It will change without notice. It might trigger earthquakes. Feedback
 /// is welcome!
+///
+/// \date 2025-11-06
+/// \author Giacomo Parolini <giacomo.parolini@cern.ch>
 
 void write_hist_to_rfile(const char *fileName)
 {

tutorials/io/experimental/rfile001_basics.py

@@ -2,16 +2,18 @@
 # \ingroup ROOT7 tutorial_io
 # Demonstrate the basic usage of RFile.
 #
-# \author Giacomo Parolini <giacomo.parolini@cern.ch>
-# \date 2025-11-06
 # \macro_code
 # \macro_output
+#
 # \warning This is part of the ROOT 7 prototype! It will change without notice. It might trigger earthquakes. Feedback
 # is welcome!
+#
+# \date 2025-11-06
+# \author Giacomo Parolini <giacomo.parolini@cern.ch>
 
 import ROOT
 
 import os
 
 def write_hist_to_rfile(fileName):
     # Create a histogram to write to the file

tutorials/machine_learning/TMVA_SOFIE_GNN.py

@@ -3,6 +3,8 @@
 ## \notebook -nodraw
 ##
 ## \macro_code
+##
+## \author
 
 import ROOT
 
@@ -10,12 +12,12 @@
 # NumPys builtin openblas later, which will conflict with the system openblas.
 ROOT.gInterpreter.Load("libopenblaso.so")
 
 import time
 
 import graph_nets as gn
 import numpy as np
 import sonnet as snt
 from graph_nets import utils_tf
 
 # defining graph properties
 num_nodes = 5

tutorials/machine_learning/TMVA_SOFIE_GNN_Application.C

@@ -1,9 +1,11 @@
 /// \file
 /// \ingroup tutorial_ml
 /// \notebook -nodraw
-// Macro evaluating a GNN model which was generated with the Parser macro
-//
-// \macro_code
+/// Macro evaluating a GNN model which was generated with the Parser macro
+///
+/// \macro_code
+///
+/// \author
 
 // need to add include path to find generated model file
 #ifdef __CLING__

tutorials/machine_learning/TMVA_SOFIE_GNN_Parser.py

@@ -6,6 +6,8 @@
 ## The tutorial also generate some  data which can serve as input for the tutorial TMVA_SOFIE_GNN_Application.C
 ##
 ## \macro_code
+##
+## \author
 
 import os
 

tutorials/math/fit/NumericalMinimization.py

@@ -1,19 +1,19 @@
-#  \file
-#  \ingroup tutorial_fit
-#  \notebook -nodraw
-#  Example on how to use the new Minimizer class in ROOT
-#   Show usage with all the possible minimizers.
-#  Minimize the Rosenbrock function (a 2D -function)
-#
-#  input : minimizer name + algorithm name
-#  randomSeed: = <0 : fixed value: 0 random with seed 0; >0 random with given seed
-#
-#  \macro_code
-#
-#  \author Lorenzo Moneta
+## \file
+##  \ingroup tutorial_fit
+##  \notebook -nodraw
+##  Example on how to use the new Minimizer class in ROOT
+##   Show usage with all the possible minimizers.
+##  Minimize the Rosenbrock function (a 2D -function)
+##
+##  input : minimizer name + algorithm name
+##  randomSeed: = <0 : fixed value: 0 random with seed 0; >0 random with given seed
+##
+##  \macro_code
+##
+##  \author Lorenzo Moneta
 
 import ROOT
 import numpy as np
 
 
 def RosenBrock(vecx):

tutorials/math/matrix/decomposeQR.C

@@ -4,6 +4,7 @@
 /// This tutorial shows how to decompose a matrix A in an orthogonal matrix Q and an upper
 /// triangular matrix R using QR Householder decomposition with the TDecompQRH class.
 /// The matrix same matrix as in this example is used: https://en.wikipedia.org/wiki/QR_decomposition#Example_2
+/// \author
 
 #include <iostream>
 #include "TMath.h"