Gabriele Cosmo
271 lines
10 KiB
Plaintext
271 lines
10 KiB
Plaintext
# $Id: README,v 1.21 2005/03/17 19:48:27 daquinog Exp $
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# -------------------------------------------------------------------
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# GEANT4 tag $Name: geant4-09-01 $
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# -------------------------------------------------------------------
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Simulation of the TIARA experiment using importance sampling
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============================================================
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This example is a simulation of the neutron shielding experiment TIARA
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see http://idsun1.kek.jp/nakao/research/tiara/tiara.htm.
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The example is meant to provide a realistic example for
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applying geometrical importance sampling (geometrical splitting and
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Russian roulette).
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In the TIARA experiment neutrons of two different energy distributions
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created by 43 MeV and 68 MeV protons bombarding a 7Li target are measured
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behind several shields. The simulation starts from the neutron spectra.
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In this example the interactions of the neutrons with the (concrete)
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shields are simulated and energy dependent neutron fluxes are measured
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behind the shields. Users may choose to run the simulation for different
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shielding configuration with or without importance sampling.
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The simulated neutron fluxes are compared to the published
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experimental data. The efficiency of applying importance sampling
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depends strongly on the shield thickness and importance configuration.
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The efficiency of the simulations may be obtained as FOM values.
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See also "Geant4 User's Guide
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For Application Developers", Chapter "Toolkit Fundamentals"
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Section "Event Biasing Techniques" and references there.
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The example has been tested on CERN RH 7.3 with the
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gcc-3.2.3 and gcc-2.95.2 compilers.
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Note for system testing without analysis: see points 1.1, 1.3 and 3.2.
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1) Setting the environment
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==========================
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You must compile Geant4 with:
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"G4LIB_BUILD_SHARED" set to 1.
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You need to set:
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"NeutronHPCrossSections" (e.g. pointing to a directory containing G4NDL3.7)
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Three examples are given of how to set environment variables
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in the three cases:
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1.1) PI via AFS (most simple):
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--------------------
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You have access to afs and you want to use PI for the
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analysis:
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Set the following environment variables:
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"G4ANALYSIS_USE"
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"PI_BASE_DIR" (where PI has been installed)
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"SWIG_BASE_DIR" (where SWIG has been installed)
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Finally source the script envCommon.csh from the directory where you have Tiara
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installed, e.g. ${G4INSTALL}/examples/advanced/Tiara
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1.2) No afs but a local PI and SWIG installation:
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-----------------------------------------------------
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You don't use afs but you have a local installation of SWIG and PI
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Set the following environment variables:
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"G4ANALYSIS_USE"
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"PI_BASE_DIR"
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"SWIG_BASE_DIR"
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Finally source the script envCommon.csh from the directory where
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you have Tiara installed, e.g. ${G4INSTALL}/examples/advanced/Tiara
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1.3) No afs, no PI and no SWIG (e.g. for system testing):
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-------------------------------------------------------------
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You don't want to use PI and SWIG and you don't have afs access.
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Make sure "G4ANALYSIS_USE" is unset (e.g. unsetenv G4ANALYSIS_USE or setenv G4ANALYSIS_USE 0)
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Note 1: python must be built against a "libc".
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You may do this by creating a shared library by a command
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similar to this
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"ld -shared -o libpython2.3.so --whole-archive libpython2.3.a /usr/lib/libc.so"
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Set the following environment variables:
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"PYTHONVERSION" (2.3 or higher)
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"PYTHON_BASE_DIR" (e.g. to /usr)
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"SWIG_VERSION" (e.g. 1.3 or higher)
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"SWIG_BASE_DIR" (e.g. /usr)
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"CLHEP_BASE_DIR" (e.g. to /opt/local)
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Finally source the script envCommon.csh from the directory where
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you have Tiara installed, e.g. ${G4INSTALL}/examples/advanced/Tiara
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NOTE 2: Passing from a simulation WITH to another WITHOUT analysis implies
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that the G4ANALYSIS_use should be unset and that the SWIG-wrapper scripts
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in the directory TiaraWrapper should be deleted. Normally, the whole code
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should be compiled again using gmake clean_all and, afterwards, gmake.
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The same procedure should be followed also passing from a simulation WITHOUT
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to another WITH analysis.
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2) Building the example
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=======================
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Geant4 has to be compiled using G4LIB_BUILD_SHARED 1 and the one of the
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above environment settings have to be set.
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Type "gmake".
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2.1) In more detail
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-------------------
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Geant4 must be compiled into granular shared libraries.
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Therefore before compiling Geant4 set "G4LIB_BUILD_SHARED" to "1".
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External packages used in this example are PI, SWIG and Python.
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2.2) Cleaning up
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----------------
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To remove the files created by swig ( *_wrap.cc, the corresponding
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.py files) use "gmake swigClean".
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To remove the directories of compiled code related to this example under
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$G4WORKDIR/tmp/$G4SYSTEM/ use "gmake tiaraClean".
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To remove all the files created during the previous compilation, use
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"gmake clean_all".
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3) Running the example:
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=======================
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3.1) Using PI
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-----------------
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Change to the sub directory "run" and execute "runSim.py".
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"runSim.py" runs an example configuration which may be
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changed by the user. The results of the simulation are stored in
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the directory "simData".
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The script "runSim.py" periodically prints scoring information to the
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screen.
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It also prints out the relative path of a ".shelve" file.
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This file can be used to access results of the simulation.
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In case the example was build using analysis
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results may be viewed e.g. using python or ROOT:
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1) Start python
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2) type: import dataAcess
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3) type: p = dataAcess.ExpMcPlot (shelveFileName,detectorposition)
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where "shelveFileName" is the name (enclosed in quotes) of the
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shelve file created during the execution of runSim.py.
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The name is printed on the screen during execution of runSim.py.
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"detectorposition" may be "00, "20" or "40" and stand for the
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detectors 00, 20, 40 cm off beam axis respectively.
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4) type: p.display ()
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5) type: import extractShelve
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6) type: extractShelve.getFluxes (p.she)
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p.she is the shelve object containing simulation results.
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This prints the FOM values for the used detectors
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for two energy regions below and in the peak region.
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The FOM should be compared to unbiased simulations. To run
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an unbiased simulation set the importance values to one (see
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runSim.py)
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Note: the output files written after every run are incremental! Meaning
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the file with the highest run number contains the data from
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all the runs!
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The modules (dataAcess, extractShelve) imported in the above steps are
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placed in source/py_modules in files named after the module ending with
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".py".
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Since the simulation time used by "runSim.py" is only 5 minutes the
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calculated results have large errors. If you want to see a better result
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do the following changes in "runSim.py".
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Extend the total run time to e,g, 2 hours by setting:
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"totalTime = 2 * myUtils.hour",
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set the print out period to 30 minutes by setting:
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"timeForOneRun = 30 * myUtils.min".
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3.2) Not using PI (mainly for system testing)
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-------------------------------------------------
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Change to the sub directory "run" and execute
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runSimNoAnalysis.py.
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4) Content of the directory:
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============================
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envCommon.csh - a script to setup environment variables
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in case the above one of the above environment settings have been
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done beforehand.
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GNUmakefile - for building the example
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data / - directory containing experimental data
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expDataOrig - experimental data taken from
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http://idsun1.kek.jp/nakao/research/tiara/tiara.htm
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expDataConverted - some of the experimental data and the source
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spectra converted into Anaphes DataPointSet
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and stored in Anaphes xml format
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source / - directory containing the source code
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tiara - C++ source code
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CLHEPWrapper - wrapper classes for usage in python
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\include\CLHEP.i - specification file for swig
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\src\CLHEP_wrap.cc - wrapper created with swig
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G4KernelWrapper - wrapper classes for usage in python
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TiaraWrapper - wrapper classes for usage in python
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py_modules - python modules for running and analysing
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swig.gmk - makefile rules for using swig
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run / - directory for running the example
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runSim.py - executable example script
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runSimNoAnalysis.py - executable script for running without
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analysis
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5) Technicalities about this example
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===================================
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This example is composed out of a layer of classes written in C++
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(source/tiara/include, source/tiara/src), a layer of python
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shadow classes created by swig (source/CLHEPWrapper,
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source/G4KernelWrapper, source/TiaraWrapper) and a layer of
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python modules (source/py_modules).
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The C++ layer under source/tiara provides lower level classes closely
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related to the Geant4 kernel.
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The shadow classes in the source/*Wrapper directories provide classes
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that may be instantiated in a python script or session. This way the
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lower level C++ code can be used directly for scripting in python. The
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shadow classes are declared in the files with extensions ".i" in the
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source/*Wrapper/include directories. Swig is used to create C++ code for
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a library that can be loaded into python and python modules (files with
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extensions .py in the source/*Wrapper directories). When the python
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modules created by swig are imported into python (in a session or a
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script) the corresponding C++ library is automatically loaded as well.
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The python modules in source/py_modules provide classes and functions
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that may be changed or customized more frequently than the C++ classes
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in source/tiara. These classes complete the construction of geometries,
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take care of the procedure of setting up the simulation, the run sequence
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and the analysis after the simulation has been ran.
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The script runSim.py is an commented example of using the python modules
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to run a simulation.
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6) Note about how to call the hadronic physics lists in runSim.py and runSimNoAnalysis.py
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=========================================================================================
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It is only necessary to change the physList value in runSim.py or runSimNoAnalysis.py. In the example, there are 4 available physics lists, but of course the user can use whatever physics lists he/she likes and present in the Geant4-7.0 repository. In such a case, the user should also modify the
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/source/TiaraWrapper/include/Tiara.i file in order to create an instantiation of the
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physics list class. In particular, just follow the same lines already used for the six
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available physics lists.
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