From 5737b4520d5a799ef754bd683fca83795d4069d3 Mon Sep 17 00:00:00 2001 From: Olivier Couet Date: Wed, 5 Feb 2025 14:53:33 +0100 Subject: [PATCH] [skip-ci] fix doxygen warnings #17626 --- math/foam/src/TFoam.cxx | 2 +- tree/dataframe/src/RDataFrame.cxx | 8 ++++---- tutorials/analysis/dataframe/index.md | 8 ++++---- tutorials/hist/index.md | 4 ++-- 4 files changed, 11 insertions(+), 11 deletions(-) diff --git a/math/foam/src/TFoam.cxx b/math/foam/src/TFoam.cxx index a6cf9ed8b8476..1514d5026ad92 100644 --- a/math/foam/src/TFoam.cxx +++ b/math/foam/src/TFoam.cxx @@ -42,7 +42,7 @@ See [full version of FOAM](http://jadach.home.cern.ch/jadach/Foam/Index.html) ### Simple example of the use of FOAM: Begin_Macro(source) -../../../tutorials/foam/foam_kanwa.C +../../../tutorials/math/foam/foam_kanwa.C End_Macro ### Canonical nine steering parameters of FOAM diff --git a/tree/dataframe/src/RDataFrame.cxx b/tree/dataframe/src/RDataFrame.cxx index d97140ddfbe27..e44c4f7c5d0d4 100644 --- a/tree/dataframe/src/RDataFrame.cxx +++ b/tree/dataframe/src/RDataFrame.cxx @@ -61,12 +61,12 @@ You can directly see RDataFrame in action in our [tutorials](https://root.cern/d ## Table of Contents - [Cheat sheet](\ref cheatsheet) -- [Introduction](\ref introduction) +- [Introduction](\ref rdf_intro) - [Crash course](\ref crash-course) - [Working with collections](\ref collections) - [Transformations: manipulating data](\ref transformations) - [Actions: getting results](\ref actions) -- [Distributed execution in Python](classROOT_1_1RDataFrame.html#distrdf) +- [Distributed execution in Python](rdf_distrdf) - [Performance tips and parallel execution](\ref parallel-execution) - [More features](\ref more-features) - [Systematic variations](\ref systematics) @@ -168,7 +168,7 @@ These operations do not modify the dataframe or book computations but simply ret | GetNSlots() | Return the number of processing slots that RDataFrame will use during the event loop (i.e. the concurrency level). | | SaveGraph() | Store the computation graph of an RDataFrame in [DOT format (graphviz)](https://en.wikipedia.org/wiki/DOT_(graph_description_language)) for easy inspection. See the [relevant section](\ref representgraph) for details. | -\anchor introduction +\anchor rdf_intro ## Introduction Users define their analysis as a sequence of operations to be performed on the dataframe object; the framework takes care of the management of the loop over entries as well as low-level details such as I/O and parallelization. @@ -670,7 +670,7 @@ auto graph = df2.Graph("x","y"); The `Graph` action is going to request first the value from column "x", then that of column "y". Specifically, the order of execution of the operations of nodes in this branch of the computation graph is guaranteed to be top to bottom. -\anchor distrdf +\anchor rdf_distrdf ## Distributed execution RDataFrame applications can be executed in parallel through distributed computing frameworks on a set of remote machines diff --git a/tutorials/analysis/dataframe/index.md b/tutorials/analysis/dataframe/index.md index 159d3bf0aa38c..7159f113c5fb1 100644 --- a/tutorials/analysis/dataframe/index.md +++ b/tutorials/analysis/dataframe/index.md @@ -20,18 +20,18 @@ histoB->Draw(); // HistoB has already been filled, no event loop is run here Explore the examples below or go to [RDataFrame's user guide](classROOT_1_1RDataFrame.html). A list of all the RDataFrame tutorials can be found [here](\ref alltutorials). ## Table of contents -- [Introduction](\ref introduction) +- [Introduction](\ref df_intro) - [Processing your data](\ref processingdata) - [Write and read from many sources](\ref readwrite) - [Interface with Numpy and Pandas](\ref numpypanda) -- [Distributed execution in Python](\ref distrdf) +- [Distributed execution in Python](\ref df_distrdf) - [Know more about your analysis](\ref analysisinfo) - [Example HEP analyses tutorials](\ref hepanalysis) - [List of all the tutorials](\ref alltutorials) -\anchor introduction +\anchor df_intro ## Introduction To get started these examples show how to create a simple RDataFrame, how to process the data in a simple analyses and how to plot distributions. @@ -96,7 +96,7 @@ From Python, NumPy arrays can be imported into RDataFrame and columns from RData | df032_RDFFromNumpy.py | Read data from Numpy arrays. | | df035_RDFFromPandas.py | Read data from Pandas DataFrame. | -\anchor distrdf +\anchor df_distrdf ##Distributed execution in Python RDataFrame applications can be executed in parallel through distributed computing frameworks on a set of remote machines via Apache Spark or Dask. diff --git a/tutorials/hist/index.md b/tutorials/hist/index.md index c0af4d4334779..00adda8d42105 100644 --- a/tutorials/hist/index.md +++ b/tutorials/hist/index.md @@ -37,7 +37,7 @@ Explore the examples below for [different histogram classes](group__Histograms.h ## Tutorials sorted after groups -- [Introduction](\ref introduction) +- [Introduction](\ref hist_intro) - [TH1 Histograms](\ref th1) - [TH2 Histograms](\ref th2) - [THnSparse](\ref thnsparse) @@ -48,7 +48,7 @@ Explore the examples below for [different histogram classes](group__Histograms.h - [TExec](\ref texec) [List of all tutorials](\ref alltutorials) -\anchor introduction +\anchor hist_intro ## Introduction