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Event generation on the farm with CMKIN
Make
sure that you followed the steps explained in the previous
section.
The cmkin version we are using is 2_1_0. To build an executable
first change to the test area that we set up then do the following
steps
cd
$TOP
cmscvsroot CMKIN
You
will need to login the first time using cvs
login and password:
98passwd
cvs
co -r CMKIN_2_1_0 -d CMKIN_2_1_0 CMKIN
cd
CMKIN_2_1_0/examples/make_ntpl_jobs/
setenv SCRATCH $PWD
project CMKIN CMKIN_2_1_0
kine_make_ntpl_pyt.com
Will
create kine_make_ntpl_pyt6220.exe which is basically a pythia
executable with the difference that
-
it has an interface to change the generator parameters without
recompiling.
- the output of the results is in form of hepevt
ntuples. In addition there are also the scripts:
kine_make_ntpl_compHEP.com
kine_make_ntpl_isa.com
kine_make_ntpl_hwg.com
kine_make_ntpl_pyt.com
which
create executables for different event generators (ISAJET,
HERWIG, COMPHEP)
Now
we are ready to
produce some Monte Carlo
data using the scripts we created in the
previous section.
Now
we are ready to submit our job into the queue. The following
command
will request 8 simultaneous processes, the run numbers
of the created files will start at 10000,
we request 100 events per process.
cd
$SCRIPTS
setup kerberos
setup fbsng
fbs submit zprime_jj_cmkin.jdf 8 10000 100
The
Batch system should respond:
Farm
Job <xxxx> has been
submitted...
You
can check the status of your batch ob by issuing the
fbs
status <xxxx>
command.
This job should finish pretty quickly and you should receive
email from the batch system to
the mail adress that you specified in the when issuing
the setup.pl command in the previous section. If everything
went ok it should
say
Exit
Code:0
and
you should find the ntpl and root files in dcache in the pnfs
directory created in the previous section.
ls
${PNFS_PATH}/<runnumber>
In
this case the run numbers would be 10001 to 10008.
If
you didn't change anything in the .jdf file all the error and
output messages
of
all your
processed can be
found in:/storage/data/fbs-logs You can
list them by typing:
ls
/storage/data/fbs-logs/FBS_<xxxx>*
We
want to use root to analyze the data that we produced. In this
simple example
we calculate
the
invariant
mass, of the dimuon
and create pt and rapidity distributions.
First copy the .root file out of dcache
and setup
the root environment
(use the same
root version as used by ORCA).
cd
$ANALYSIS
set i=10001
while ($i < 10009)
dccp $PNFS_PATH/${i}/zprime_jj_${i}.root
.
@ i++
end
ROOT is used by ORCA therefore it
is setup with the ORCA runtime environment.
To set this up:
scram
list ORCA
cd /afs/fnal.gov/files/code/cms/l/Releases/ORCA/ORCA_7_6_0
eval `scram runtime -csh`
cd -
Or,
you may run source ${TOP}/setup_root.csh
Then
start ROOT and execute the provided cint macros:
root
root [0] .L h101.C
root [1] .x plot.C
root [2]
will
produce the plots below. The first row shows the invariant
mass
and pt
distribution of the
Z'. row 2
and 3 show the
pt, rapidity and pseudo
rapidity
distribution of the mu+ and mu- respectively.
Note the plots were obtained
from a previous
run
with
more
statistics than in the example
files.
To
speed up the execution time of your macros you might want
to compile the script (using the root ACLiC compiler). In addition
to speed, the advantage of using ACLiC is that the syntax
of
the script is checked by the compiler. Many bugs and non-standard
C++ syntax that Cint accepts will be caught that way and
it will be easier to convert the script to a C++ program later
on. The ROOT interpreter allows a syntax which is not compliant
to C++ and should be avoided.
To
do that use the following syntax:
root
root
[0] .L h101.C+
root [1] .x plot.C+
root [2]
To create h101.C and h101.h I used the root MakeClass() function
and then added the histograms to be filled in the loop. If
you want to create your own class here is how it is done:
Following
the procedure below the following files are produced: h101.h
and h101.C
The generated code in h101.h includes the following:
- Identification
of the original Tree and Input file name
- Definition of analysis class (data and functions)
- the following class functions:
- constructor
(connecting by default the Tree file)
- GetEntry(Int_t entry)
- Init(TTree *tree) to initialize a new TTree
- Show(Int_t entry) to read and Dump entry
- Loop() loops over all entries, this is the place where
you want to add your analysis
For
more information visit the ROOT site: http://root.cern.ch/
Below
is an example of how to use MakeClass for one input file:
root
[0] TFile *f = new TFile("zprime_jj_10001.root");
root [1] f->ls();
TFile** zprime_jj_10001.root HBOOK file: zprime_jj_10001.ntpl
converted to ROOT
TFile* zprime_jj_10001.root HBOOK file: zprime_jj_10001.ntpl
converted to ROOT
KEY: TTree h101;1 HEPEVT
root [2] h101->MakeClass();
Info in <TTreePlayer::MakeClass>: Files: h101.h and
h101.C generated from Tree: h101
Similarly,
if you want to analyze several files simultaneously you can
chain them together and then create the classes for the chain (in this
case the
first 10) in the following way:
root
TChain
h101("h101");
h101.Add("zprime_jj_10001.root");
h101.Add("zprime_jj_10002.root");
h101.Add("zprime_jj_10003.root");
h101.Add("zprime_jj_10004.root");
h101.Add("zprime_jj_10005.root");
h101.Add("zprime_jj_10006.root");
h101.Add("zprime_jj_10007.root");
h101.Add("zprime_jj_10008.root");
h101.MakeClass();
1. General
setup
2. Event generation on the farm with CMKIN (Pythia),
Analysis of the generator level output
3. Simulate the events with OSCAR
4. Digitize with ORCA
5. Run a small ntuple maker ( from Stephan's
ORCA tutorial) looking at Calorimeter quantities
6. Run the JetMet ntuple maker and make some plots
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