Editing Results (section)

==Writing Root Macro==

In order to simplify and streamline the analysis, a macro can be designed to read in multiple files at one time and produce several histograms of the data. Furthermore, fitting the data and illustrating the length dependence for multiple trials helps in visualizing possible errors in the data or in the analysis.

=== Creation ===

Root provides a data analysis framework that allows for program creation using a built-in C++ interpreter. Becoming familiar with Root will be key in writing functional macros that will efficiently and easily analyze our data.

Some helpful documentation has been provided by CERN to aid in learning specific functions of the framework. For instance, the following link provides an example for basic graphing syntax:

http://root.cern.ch/root/html/tutorials/graphs/graph.C.html

This type of example-based documentation may prove to be invaluable in creating Root macros.

=== Helpful Starting Tips ===

For the purposes of this experiment, histograms, graphs, fitting functions, and file I/O are going to be key for the data analysis. Below are some helpful hints on how to design such a program:

'''1. Have a Plan'''

Creating a program needs some preparation. Thinking about the end result and what you intend to produce will help greatly in designing an efficient macro. For instance, planning out possible variables, methods for parsing the data, and displaying the data can really help guide one through the creation process. 

'''2. Scope'''

Along with the planning, ideas around how much data you are handling will help in deciding how to create a certain function. For example, say one experiment required five histograms to be created along with a single plot of average time versus cable length. This would be easy to simply have 5 separate Canvases with 5 different histograms and then a second Canvas to display the average time plot. But if there were 50 histograms that were going to be analyzed, a different approach would be needed for the creation of the histograms and how they are going to be displayed.

'''3. Attempt''' 

Usually, a first attempt is never perfect, so do not be afraid to try something. Trial and error is going to be the best form of practice in discovering the best way to conduct the data analysis.

=== Useful Syntax ===

'''1. Having a Root File''' 

Machine-Independent binary file creation: ''TFile *f = new TFile("junk2.root","RECREATE");''

Helpful link: http://root.cern.ch/drupal/content/accessing-root-file

'''2. Creating Histograms'''

To create a single histogram pointer: ''TH1F *h1 = new TH1F("h1","x distribution",10000,0.0,1.0);'' 

'''3. Creating Canvases'''

Single area mapped to a window for something to be displayed: ''TCanvas *c1 = new TCanvas("c1","Hists with different scales",2000,1000);''

Helpful link: http://root.cern.ch/root/html/TCanvas.html

'''4. Pads'''

Dividing the canvas into columns and rows: ''c1->Divide(4,5);''

Helpful link: http://root.cern.ch/drupal/content/canvases-and-pads

'''5. File I/O'''

String formatting might be a possible way of scanning each file: ''sprintf(name, "exp0_trial'''%d'''.txt",1);''

Scanning file: ''fscanf(fp,"%f %f",&'''column1''', &'''column2''');''

Helpful hint: TString has a function called Form() that can manipulate strings.

'''6. Graphing'''

Basic graph creation: ''graph = new TGraph(2,length,average_time);''

Helpful link: ftp://root.cern.ch/root/doc/4Graphs.pdf