.. -- mode: rst --
The master branch is now dedicated to analysis of the new atlas data format (xAOD). For production of skims out of the old format (D3PD), please refer to the d3pd branch.
Latest 5.X ROOT <http://root.cern.ch/drupal/>
_ with PyROOT enabled.
rootpy <https://github.com/rootpy/rootpy>
_::
git clone git://github.com/rootpy/rootpy.git cd rootpy python setup.py install --user
goodruns <http://pypi.python.org/pypi/goodruns>
_::
pip install --user goodruns
PyYAML <https://pypi.python.org/pypi/PyYAML>
_::
pip install --user pyyaml
ConfigObj <http://www.voidspace.org.uk/python/configobj.html>
_::
pip install --user configobj
externaltools <https://github.com/htautau/externaltools>
_::
git clone git://github.com/htautau/externaltools.git
lumi <https://github.com/htautau/lumi>
_::
git clone git://github.com/htautau/lumi.git
hhntup <https://github.com/htautau/hhntup>
_::
git clone git://github.com/htautau/hhntup.git
TauSpinnerTool <https://svnweb.cern.ch/trac/atlasoff/browser/PhysicsAnalysis/TauID/TauSpinnerTool>
_::
svn co svn+ssh://${USER}@svn.cern.ch/reps/atlasoff/PhysicsAnalysis/TauID/TauSpinnerTool/trunk TauSpinnerTool
Place externaltools, lumi, and TauSpinnerTool in the same directory containing hhntup to satisfy the symlinks in hhana. See the README in externaltools and TauSpinnerTool for further instructions. Use at least Python version 2.6 (2.7 is preferred).
Analysis release used::
Base, 2.0.17
Dataset used::
mc14_8TeV.147808.PowhegPythia8_AU2CT10_Ztautau.merge.AOD.e2372_s1933_s1911_r5591_r5625
To run the test::
skim --local-test mc12_hadhad_xaod
Now build the C extension module for jet cleaning in the higgstautau package::
make lib
Before running tests locally::
source setup.sh
The skimming is performed by the hhskim.py
script.
Run the skims on the grid (after setting up the panda client and your VOMS proxy with the phys-higgs production role)::
./skim --yall mc11_hadhad mc12_hadhad \
data11_hadhad data12_hadhad \
embed11_hadhad embed12_hadhad
The samples are organized in several blocks defined in skims.cfg
.
Each block is written following the template::
[block_name] student = hhskim.py dataset = dataset_block (defined in datasets.cfg) version = version_number testinput = /path/to/the/input/files/for/test dest = SFU-LCG2_LOCALGROUPDISK,
For each block, modify the variable testinput according to your own setup.
Run the test::
./skim --yall block_name --local-test
The output will be created in the main directory as::
hhskim_dataset_block.root
After the skims are finished and downloaded, update the paths in
higgstautau/datasets_config.yml
and update the datasets database::
./dsdb --reset hh
Then launch the batch jobs that create all the analysis ntuples (nominal and systematics) with::
./run-all