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examples/extended/analysis/B1Con/.README.txt
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///\file "analysis/B1Con/.README.txt"
///\brief Example B1Con README page
/*! \page ExampleB1Con Example B1Con
Example of Convergence Tester
Koi, Tatsumi \n
SLAC National Accelerator Laboratory / PPA \n
tkoi@slac.stanford.eedu \n
This example shows how to use convergece tester in Geant4.
The aim of Convergence Tester
- After a Monte Carlo simulation, we get an answer. However how to estimate quality of the answer.
The answer is usually given in a form of average value.
But sometimes the value is strongly affected by single or a few events in the full calculation.
In such case, we must concern about quality of the value.
What we must remember is
- Large number of history does not valid result of simulation.
- Small Relative Error does not valid result of simulation
Convergence tester provides statistical information
to assist establishing valid confidence intervals for Monte Carlo results for users.
Geometry and Physics are same to exampleB1. Please see \ref ExampleB1.
Note that in this example, the classes with the code added for
the purpose of demonstration of the Convergence Tester start with a prefix
B1Con instead of B1 and also the executable and the test macro names are changed
in exampleB1Con and exampleB1Con.in.
Known problem:
Computing time of T cannot be gotten properly in current MT migration of example of B1Con. Therefore
FOM (=1/(R^2T) where R is relative error and T is computing time) relates numbers are unusable.
\verbatim
***********************************************************************************************************************
Output example
// Part I.A
// Basic statistics values
G4ConvergenceTester Output Result of DOSE_TALLY
EFFICIENCY = 0.601
MEAN = 4.81721e-12
VAR = 2.15334e-23
SD = 4.64041e-12
R = 0.0304622
SHIFT = 2.22459e-13
VOV = 0.000166754
FOM = 1238.68
// Part I.B
// If the largeset scored events happen at next to the last event,
// then how much the event effects the statistics values of the calculation
THE LARGEST SCORE = 1.07301e-11 and it happend at 487th event
Affected Mean = 4.82311e-12 and its ratio to orignal is 1.00123
Affected VAR = 2.15468e-23 and its ratio to orignal is 1.00062
Affected R = 0.0304192 and its ratio to orignal is 0.998587
Affected SHIFT = 2.1804e-13 and its ratio to orignal is 0.980133
Affected FOM = 1238.68 and its ratio to orignal is 1
// Part I.C
// Convergence tests results
MEAN distribution is RANDOM
r follows 1/std::sqrt(N)
r is monotonically decrease
r is less than 0.1. r = 0.0304622
VOV follows 1/std::sqrt(N)
VOV is monotonically decrease
FOM distribution is not RANDOM
SLOPE is not large enough
This result passes 6 / 8 Convergence Test.
// Part II
// Profile of statistics values in the history
G4ConvergenceTester Output History of DOSE_TALLY
i/16 till_ith mean var sd r vov fom shift e r2eff r2int
1 62 4.94618e-12 2.04631e-23 4.52362e-12 0.115225 0.00313634 86.5745 -1.73435e-14 0.619048 0.00976801 0.00329797
2 124 4.69364e-12 2.10698e-23 4.59018e-12 0.0874712 0.001597 150.228 3.11143e-13 0.6 0.00533333 0.00225666
3 187 4.72161e-12 2.14009e-23 4.62612e-12 0.0714575 0.00101852 225.105 3.1009e-13 0.590426 0.00368986 0.00138916
4 249 4.95617e-12 2.13982e-23 4.62582e-12 0.0590299 0.000690138 329.865 9.71971e-14 0.62 0.00245161 0.00101898
5 312 4.8529e-12 2.13482e-23 4.62041e-12 0.0538155 0.000573301 396.887 1.95662e-13 0.607029 0.00206827 0.000818582
6 374 5.14255e-12 2.15736e-23 4.64474e-12 0.046641 0.000432121 528.379 -6.42963e-14 0.637333 0.00151743 0.000652145
7 437 5.03849e-12 2.13484e-23 4.62043e-12 0.0438173 0.000379317 598.673 2.54207e-14 0.636986 0.00130112 0.000614447
8 499 4.96962e-12 2.1429e-23 4.62914e-12 0.0416574 0.000329007 662.364 9.27708e-14 0.63 0.0011746 0.000557264
9 562 4.91513e-12 2.14709e-23 4.63367e-12 0.0397316 0.000285324 728.13 1.33544e-13 0.623446 0.0010728 0.000502991
10 624 4.82995e-12 2.13825e-23 4.62412e-12 0.0382954 0.000272664 783.766 2.19101e-13 0.616 0.000997403 0.000466792
11 687 4.79197e-12 2.13975e-23 4.62574e-12 0.0368022 0.000251788 848.661 2.48547e-13 0.606105 0.000944593 0.000407838
12 749 4.77183e-12 2.15116e-23 4.63807e-12 0.0354912 0.000227501 912.513 2.6728e-13 0.601333 0.000883962 0.000373986
13 812 4.76087e-12 2.14479e-23 4.63119e-12 0.0341162 0.000212259 987.548 2.70437e-13 0.597786 0.000827601 0.000334885
14 874 4.81359e-12 2.13296e-23 4.6184e-12 0.0324353 0.0001976 1092.56 2.14521e-13 0.603429 0.000751082 0.000299767
15 937 4.82018e-12 2.14558e-23 4.63204e-12 0.0313767 0.000181379 1167.52 2.18545e-13 0.601279 0.000706952 0.000276498
16 999 4.81721e-12 2.15334e-23 4.64041e-12 0.0304622 0.000166754 1238.68 2.22459e-13 0.601 0.000663894 0.000263125
**************************************************************************************************************************
\endverbatim
Reference of this Convergence tests: \n
MCNP(TM) -A General Monte Carlo N-Particle Transport Code \n
Version 4B \n
Judith F. Briesmeister, Editor \n
LA-12625-M, Issued: March 1997, UC 705 and UC 700 \n
CHAPTER 2. GEOMETRY, DATA, PHYSICS, AND MATHEMATICS \n
VI. ESTIMATION OF THE MONTE CARLO PRECISION \n
*/
examples/extended/analysis/B1Con/README
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Example of Convergence Tester
Koi, Tatsumi
SLAC National Accelerator Laboratory / PPA
tkoi@slac.stanford.eedu
This example shows how to use convergece tester in Geant4.
The aim of Convergence Tester
After a Monte Carlo simulation, we get an answer. However how to estimate quality of the answer.
The answer is usually given in a form of average value.
But sometimes the value is strongly affected by single or a few events in the full calculation.
In such case, we must concern about quality of the value.
What we must remember is
Large number of history does not valid result of simulation.
Small Relative Error does not valid result of simulation
Convergence tester provides statistical information
to assist establishing valid confidence intervals for Monte Carlo results for users.
Geometry and Physics are same to exampleB1. Please see README.B1
Note that in this example, the classes with the code added for
the purpose of demonstration of the Convergence Tester start with a prefix
B1Con instead of B1 and also the executable and the test macro names are changed
in exampleB1Con and exampleB1Con.in.
Known problem:
Computing time of T cannot be gotten properly in current MT migration of example of B1Con. Therefore
FOM (=1/(R^2T) where R is relative error and T is computing time) relates numbers are unusable.
***********************************************************************************************************************
Output example
// Part I.A
// Basic statistics values
G4ConvergenceTester Output Result of DOSE_TALLY
EFFICIENCY = 0.601
MEAN = 4.81721e-12
VAR = 2.15334e-23
SD = 4.64041e-12
R = 0.0304622
SHIFT = 2.22459e-13
VOV = 0.000166754
FOM = 1238.68
// Part I.B
// If the largeset scored events happen at next to the last event,
// then how much the event effects the statistics values of the calculation
THE LARGEST SCORE = 1.07301e-11 and it happend at 487th event
Affected Mean = 4.82311e-12 and its ratio to orignal is 1.00123
Affected VAR = 2.15468e-23 and its ratio to orignal is 1.00062
Affected R = 0.0304192 and its ratio to orignal is 0.998587
Affected SHIFT = 2.1804e-13 and its ratio to orignal is 0.980133
Affected FOM = 1238.68 and its ratio to orignal is 1
// Part I.C
// Convergence tests results
MEAN distribution is RANDOM
r follows 1/std::sqrt(N)
r is monotonically decrease
r is less than 0.1. r = 0.0304622
VOV follows 1/std::sqrt(N)
VOV is monotonically decrease
FOM distribution is not RANDOM
SLOPE is not large enough
This result passes 6 / 8 Convergence Test.
// Part II
// Profile of statistics values in the history
G4ConvergenceTester Output History of DOSE_TALLY
i/16 till_ith mean var sd r vov fom shift e r2eff r2int
1 62 4.94618e-12 2.04631e-23 4.52362e-12 0.115225 0.00313634 86.5745 -1.73435e-14 0.619048 0.00976801 0.00329797
2 124 4.69364e-12 2.10698e-23 4.59018e-12 0.0874712 0.001597 150.228 3.11143e-13 0.6 0.00533333 0.00225666
3 187 4.72161e-12 2.14009e-23 4.62612e-12 0.0714575 0.00101852 225.105 3.1009e-13 0.590426 0.00368986 0.00138916
4 249 4.95617e-12 2.13982e-23 4.62582e-12 0.0590299 0.000690138 329.865 9.71971e-14 0.62 0.00245161 0.00101898
5 312 4.8529e-12 2.13482e-23 4.62041e-12 0.0538155 0.000573301 396.887 1.95662e-13 0.607029 0.00206827 0.000818582
6 374 5.14255e-12 2.15736e-23 4.64474e-12 0.046641 0.000432121 528.379 -6.42963e-14 0.637333 0.00151743 0.000652145
7 437 5.03849e-12 2.13484e-23 4.62043e-12 0.0438173 0.000379317 598.673 2.54207e-14 0.636986 0.00130112 0.000614447
8 499 4.96962e-12 2.1429e-23 4.62914e-12 0.0416574 0.000329007 662.364 9.27708e-14 0.63 0.0011746 0.000557264
9 562 4.91513e-12 2.14709e-23 4.63367e-12 0.0397316 0.000285324 728.13 1.33544e-13 0.623446 0.0010728 0.000502991
10 624 4.82995e-12 2.13825e-23 4.62412e-12 0.0382954 0.000272664 783.766 2.19101e-13 0.616 0.000997403 0.000466792
11 687 4.79197e-12 2.13975e-23 4.62574e-12 0.0368022 0.000251788 848.661 2.48547e-13 0.606105 0.000944593 0.000407838
12 749 4.77183e-12 2.15116e-23 4.63807e-12 0.0354912 0.000227501 912.513 2.6728e-13 0.601333 0.000883962 0.000373986
13 812 4.76087e-12 2.14479e-23 4.63119e-12 0.0341162 0.000212259 987.548 2.70437e-13 0.597786 0.000827601 0.000334885
14 874 4.81359e-12 2.13296e-23 4.6184e-12 0.0324353 0.0001976 1092.56 2.14521e-13 0.603429 0.000751082 0.000299767
15 937 4.82018e-12 2.14558e-23 4.63204e-12 0.0313767 0.000181379 1167.52 2.18545e-13 0.601279 0.000706952 0.000276498
16 999 4.81721e-12 2.15334e-23 4.64041e-12 0.0304622 0.000166754 1238.68 2.22459e-13 0.601 0.000663894 0.000263125
**************************************************************************************************************************
Reference of this Convergence tests
MCNP(TM) -A General Monte Carlo N-Particle Transport Code
Version 4B
Judith F. Briesmeister, Editor
LA-12625-M, Issued: March 1997, UC 705 and UC 700
CHAPTER 2. GEOMETRY, DATA, PHYSICS, AND MATHEMATICS
VI. ESTIMATION OF THE MONTE CARLO PRECISION
examples/extended/analysis/B1Con/README.B1
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-------------------------------------------------------------------
=========================================================
Geant4 - an Object-Oriented Toolkit for Simulation in HEP
=========================================================
Example B1
-----------
This example demonstrates a very simple application where an energy
deposit is accounted in user actions and their associated objects
and a dose in a selected volume is calculated.
1- GEOMETRY DEFINITION
The geometry is constructed in the B1DetectorConstruction class.
The setup consists of a an envelope of box shape containing two
volumes: a spherical cone and a trapezoid.
In this example we use some common materials materials for medical
applications. The envelope is made of water and the two inner volumes
are made from tissue and bone materials.
The materials are created with the help of the G4NistManager class,
which allows to build a material from the NIST database using their
names. All available materials can be found in the Geant4 User's Guide
for Application Developers, Appendix 10: Geant4 Materials Database.
2- PHYSICS LIST
The particle's type and the physic processes which will be available
in this example are set in the QBBC physics list. This physics list
requires data files for electromagnetic and hadronic processes.
See more on installation of the datasets in Geant4 Installation Guide,
Chapter 3.3: Note On Geant4 Datasets:
http://geant4.web.cern.ch/geant4/UserDocumentation/UsersGuides
/InstallationGuide/html/ch03s03.html
The following datasets: G4LEDATA, G4LEVELGAMMADATA, G4NEUTRONXSDATA and
G4SAIDXSDATA are mandatory for this example.
In addition the build-in interactive command:
/process/(in)activate processName
allows to activate/inactivate the processes one by one.
3- ACTION INITALIZATION
A newly introduced class, B1ActionInitialization, instantiates and registers
to Geant4 kernel all user action classes.
While in sequential mode the action classes are instatiated just once,
via invoking the method:
B1ActionInitialization::Build()
in multi-threading mode the same method is invoked for each thread worker
and so all user action classes are defined thread-local.
A run action class is instantiated both thread-local
and global that's why its instance has is created also in the method
B1ActionInitialization::BuildForMaster()
which is invoked only in multi-threading mode.
4- PRIMARY GENERATOR
The primary generator is defined in the B1PrimaryGeneratorAction class.
The default kinematics is a 6 MeV gamma, randomly distributed in front
of the envelope across 80% of the transverse (X,Y) envelope size.
This default setting can be changed via the Geant4 built-in commands
of the G4ParticleGun class.
5- DETECTOR RESPONSE
This example demonstrates a simple scoring implemented directly
in the user action classes and B1Run object.
Alternative ways of scoring via Geant4 classes can be found in the
other examples.
The energy deposited is collected step by step for a selected volume
in B1SteppingAction and accumulated event by event in B1EventAction.
At end of event, the value acummulated in B1EventAction is added in B1Run
and summed over the whole run (see B1EventAction::EndOfevent()).
Total dose deposited is computed at B1RunAction::EndOfRunAction(),
and printed together with informations about the primary particle.
In multi-threading mode the energy accumulated in B1Run objects per
workers is merged to the master in B1Run::Merge() and the final
result is printed on the screen.
An example of creating and computing new units (e.g., dose) is also shown
in the class constructor.
The following paragraphs are common to all basic examples
A- VISUALISATION
The visualization manager is set via the G4VisExecutive class
in the main() function in exampleB1.cc.
The initialisation of the drawing is done via a set of /vis/ commands
in the macro vis.mac. This macro is automatically read from
the main function when the example is used in interactive running mode.
By default, vis.mac opens an OpenGL viewer (/vis/open OGL).
The user can change the initial viewer by commenting out this line
and instead uncommenting one of the other /vis/open statements, such as
HepRepFile or DAWNFILE (which produce files that can be viewed with the
HepRApp and DAWN viewers, respectively). Note that one can always
open new viewers at any time from the command line. For example, if
you already have a view in, say, an OpenGL window with a name
"viewer-0", then
/vis/open DAWNFILE
then to get the same view
/vis/viewer/copyView viewer-0
or to get the same view *plus* scene-modifications
/vis/viewer/set/all viewer-0
then to see the result
/vis/viewer/flush
The DAWNFILE, HepRepFile drivers are always available
(since they require no external libraries), but the OGL driver requires
that the Geant4 libraries have been built with the OpenGL option.
From Release 9.6 the vis.mac macro in example B1 has additional commands
that demonstrate additional functionality of the vis system, such as
displaying text, axes, scales, date, logo and shows how to change
viewpoint and style. Consider copying these to other examples or
your application. To see even more commands use help or
ls or browse the available UI commands in the Application
Developers Guide, Section 7.1.
For more information on visualization, including information on how to
install and run DAWN, OpenGL and HepRApp, see the visualization tutorials,
for example,
http://geant4.slac.stanford.edu/Presentations/vis/G4[VIS]Tutorial/G4[VIS]Tutorial.html
(where [VIS] can be replaced by DAWN, OpenGL and HepRApp)
The tracks are automatically drawn at the end of each event, accumulated
for all events and erased at the beginning of the next run.
B- USER INTERFACES
The user command interface is set via the G4UIExecutive class
in the main() function in exampleB1.cc
The selection of the user command interface is then done automatically
according to the Geant4 configuration or it can be done explicitly via
the third argument of the G4UIExecutive constructor (see exampleB4a.cc).
C- HOW TO RUN
- Execute exampleB1 in the 'interactive mode' with visualization:
% ./exampleB1
and type in the commands from run1.mac line by line:
Idle> /control/verbose 2
Idle> /tracking/verbose 1
Idle> /run/beamOn 10
Idle> ...
Idle> exit
or
Idle> /control/execute run1.mac
....
Idle> exit
- Execute exampleB1 in the 'batch' mode from macro files
(without visualization)
% ./exampleB1 run2.mac
% ./exampleB1 exampleB1.in > exampleB1.out