Random Numbers
This page describes the random-number generator in PYTHIA and
how it can be replaced by an external one.
Internal random numbers
The Rndm class generates random numbers, using the
Marsaglia-Zaman-Tsang algorithm [Mar90].
Random numbers R uniformly distributed in
0 < R < 1 are obtained with
Rndm::flat();
There are also methods to generate according to an exponential, to
x * exp(-x), to a Gaussian, or picked among a set of
possibilities, which make use of flat().
If the random number generator is not initialized before, it will be
so the first time it is asked to generate a random number, and then
with the default seed, 19780503. This means that, by default, all runs
will use identically the same random number sequence. This is
convenient for debugging purposes, but dangerous if you intend to
run several "identical" jobs to boost statistics. You can initialize,
or reinitialize, with your own choice of seed with a
Rndm::init(seed);
Here values 0 < seed < 900 000 000 gives so many
different random number sequences, while seed = 0 will call
the Stdlib time(0) function to provide a "random"
seed, and seed < 0 will revert back to
the default seed.
The Pythia class defines a
flag and a mode, that allows the seed to be set in
the Pythia::init call. That would be the standard way for a
user to pick the random number sequence in a run.
External random numbers
RndmEngine is a base class for the external handling of
random-number generation. The user-written derived class is called
if a pointer to it has been handed in with the
pythia.rndmEnginePtr() method. Since the default
Marsaglia-Zaman-Tsang algorithm is quite good, chances are that any
replacement would be a step down, but this may still be required by
consistency with other program elements in big experimental frameworks.
There is only one pure virtual method in RndmEngine, to
generate one random number flat in the range between 0 and 1:
virtual double flat() = 0;
Note that methods for initialization are not provided in the base
class, in part since input parameters may be specific to the generator
used, in part since initialization can as well be taken care of
externally to the Pythia code.
An example illustrating how to run with an external random number
generator is provided in main23.cc.
The methods
We here collect a more complete and formal overview of the methods.
Rndm::Rndm()
construct a random number generator, but does not initialize it.
Rndm::Rndm(int seed)
construct a random number generator, and initialize it for the
given seed number.
bool Rndm::rndmEnginePtr( RndmEngine* rndmPtr)
pass in pointer for external random number generation.
void Rndm::init(int seed = 0)
initialize, or reinitialize, the random number generator for the given
seed number. Not necessary if the seed was already set in the constructor.
double Rndm::flat()
generate next random number uniformly between 0 and 1.
double Rndm::exp()
generate random numbers according to exp(-x).
double Rndm::xexp()
generate random numbers according to x exp(-x).
double Rndm::gauss()
generate random numbers according to exp(-x^2/2).
pair<double, double> Rndm::gauss2()
generate a pair of random numbers according to
exp( -(x^2 + y^2) / 2). Is faster than two calls
to gauss().
int Rndm::pick(const vector<double>& prob)
pick one option among vector of (positive) probabilities.
bool Rndm::dumpState(string fileName)
save the current state of the random number generator to a binary
file. This involves two integers and 100 double-precision numbers.
Intended for debug purposes. Note that binary files may be
platform-dependent and thus not transportable.
bool Rndm::readState(string fileName)
set the state of the random number generator by reading in a binary
file saved by the above command. Comments as above.
virtual double RndmEngine::flat()
if you want to construct an external random number generator
(or generator interface) then you must implement this method
in your class derived from the RndmEningen base class,
to give a random number between 0 and 1.