Requirements
------------
IPython (www.ipython.org)
numpy ()
pyROOT ()
pandas ()
sci-kit learn ()
matplotlib ()

Tagger.py
Makes plots (using ROOT) of single variables and their performance as taggers

WriteCSV.py
Just writes the data (with some cuts) (using root_numpy) into csv [This stage should not exist (integrated with above or use rootpy in the below)

TaggerMVA.py
This runs an MVA in scikit

TaggerTim.py
This creates plots of single variables and their performance as taggers.  There are many options that can be given to the code:
python TaggerTim.py config -i inputfile -f fileid [--pthigh] [--ptlow] [--nvtxlow] [--nvtx] [--ptreweighting] [--saveplots] [--version] [--tree] [--massWindowCut] [--channelnumber] [--lumi]

config - first argument is always a config xml file.  Sample ones can be seen under config/.
-i - is the folder where the input files are.
-f - is the file-id that the output files will use.
pthigh/low - the pt range in GeV.  Default is 200-350 GeV
nvtx - cut on the number of primary vertices. Default is 0-99
ptreweighting - reweight the signal according to the background pt spectrum. Default is false.
saveplots - save all variables separately as well as the overview VariablesPlot and ROC curve plot.  Default is false.
-v the version number of the plotting you are running (this can be different from the file-id above). Default is ''
tree - the name of the TTree in the input files. Default is 'physics'
massWindowCut - Whether or not to apply the 68% mass window cut. Note, to run this you need to create the mass window input file - see below. Default is false.
channelnumber - To run on a run number. Default is no restriction.
lumi - The lumi scale factor. Default is 1.

Example:
	python TaggerTim.py config/nc29_fast/AntiKt10LCTopoTrimmedPtFrac5SmallR20.xml -i /media/win/BoostedBosonFiles/nc29_fast/AntiKt10LCTopoTrimmedPtFrac5SmallR20_nc29_fast_v1/ -f AntiKt10LCTopoTrimmedPtFrac5SmallR20_nc29_fast_v1 --pthigh=1000 --ptlow=500 --nvtx=99 --nvtxlow=0 --ptreweighting=true --saveplots=true -v nc29_fast_v1 --tree=physics --massWindowCut=True

When you run this it will print some stuff to screen, but will also save the rejection matrix to a pickle file with the version number (-v above) in the file name.  This pickle file
is then read in by scanBkgRej.py if that is being run.

calculateMassWindow.py
This calculates the 68% mass window for all algorithms in one folder.  It produces a text file in the algorithm folder with the suffix .masswindow which contains the mass cuts. This is read in by TaggerTim.py which then applies the mass window cuts during plotting.

python CalculateMassWindow.py foldernames foldersuffix [treename]

foldernames - This is the full path of all of the algorithms. Example: /media/win/BoostedBosonFiles/nc27_fast/AntiKt10LCTopoTrimmedPtFrac5SmallR20_nc27_fast_v1.  In scanBkgRej.py this is the same as the base directory+folder.
foldersuffix - The part added to the file name after the end of the algorithm name.  So in the example above this is _nc27_fast_v1.  This should be changed. It is only there to get a length value, but right now this is the quickest way to do it.
treename - The tree name in the input file. Default is 'physics'.

Example:
	python calculateMassWindow.py nc27_fast_folders.txt _nc27_fast_v1 physics	

scanBkgRej.py
This scans over a number of algorithms and mass ranges and runs TaggerTim.py multiple times to get the rejection values out.  To run this a number of input parameters are required.
If other parameters that are used to run TaggerTim.py need to be changed please look in the file - pt ranges for example.

To run this there must be ipython engines running.  There are two scripts that start and stop the engines.  startcluster.sh and stopcluster.sh.  This is setup to run 4 engines.  Typically it is best to stop the clusters and restart them again if there was a change made in the code as it can cache the code.

python ScanBkgRej.py -b basedirectory -f listoffolders -c listofconfigs -v version -t treename -m masswindowcut --ptlow --pthigh
b - The base directory where all of the input root files are.
f - A list of folders in the base directory that you want to run on, for example, AntiKt10LCTopoTrimmedPtFrac5SmallR20_nc27_fast_v1/ is in the base directory /home/tim/boosted_plotting/BosonTagging/.  The file is a newline separated list of folder names.  (Base directory+folder name) will get passed as the argument to TaggerTim.py -i.
c - A list of config files for each of the algorithms being run.  Note that I have not yet implemented a matching algorithm between the algorithms listed in -f and -c, so they must match up 1:1 on the same lines.  These are the config files that get passed to TaggerTim.py
v - A version of the plots.  This is what gets passed to TaggerTim.py -v.
t - Name of the TTree in the input root files.
m - Whether or not to apply the mass window cut.
ptlow/high - the pt range in GeV

Example:
	python scanBkgRej.py -b /media/win/BoostedBosonFiles/nc29_fast/ -f nc29_fast_files.txt -c nc29_fast_configs.txt -v nc29_fast_mw_v1 -t physics -m true

functions.py