RNGLIB
A Package of Random Number Generators (RNG's) with Splitting Facilities

RNGLIB is a Python library which implements random number generators (RNG's) which can generate one or more streams of random numbers.

RNGLIB is a portable set of software tools for uniform random variate generation. It provides for multiple generators running simultaneously, and each generator has its sequence of numbers partitioned into many long disjoint substreams. Simple procedure calls allow the user to make any generator jump ahead to the beginning of its next substream.

Licensing:

The computer code and data files described and made available on this web page are distributed under the GNU LGPL license.

Languages:

RNGLIB is available in a C version and a C++ version and a FORTRAN90 version and a MATLAB version and a Python version.

Related Data and Programs:

NORMAL, a Python library which contains random number generators (RNG's) for normally distributed values.

PDFLIB, a Python library which evaluates Probability Density Functions (PDF's) and produces random samples from them, including beta, binomial, chi, exponential, gamma, inverse chi, inverse gamma, multinomial, normal, scaled inverse chi, and uniform.

RANDLC, a Python library which implements a random number generator (RNG) used by the NAS Benchmark programs.

SOBOL, a Python library which computes elements of a Sobol quasirandom sequence.

UNIFORM, a Python library which implements random number generators (RNG's) for a variety of arithmetic types.

VAN_DER_CORPUT, a Python library which computes elements of a 1D van der Corput Quasi Monte Carlo (QMC) sequence using a simple interface.

Reference:

1. Pierre LEcuyer, Serge Cote,
   Implementing a Random Number Package with Splitting Facilities,
   ACM Transactions on Mathematical Software,
   Volume 17, Number 1, March 1991, pages 98-111.

Source Code:

• advance_state.py advances the state of a generator.
• antithetic_get.py queries the antithetic value for a given generator.
• antithetic_memory.py stores the antithetic value for all generators.
• antithetic_set.py sets the antithetic value for a given generator.
• cg_get.py queries the CG values for a given generator.
• cg_memory.py stores the CG values for all generators.
• cg_set.py sets the CG values for a given generator.
• cgn_get.py gets the current generator index.
• cgn_memory.py stores the current generator index.
• cgn_set.py sets the current generator index.
• get_state.py returns the state of generator G.
• i4_uni.py generates a random positive integer.
• ig_get.py queries the IG values for a given generator.
• ig_memory.py stores the IG values for all generators.
• ig_set.py sets the IG values for a given generator.
• init_generator.py sets the state of generator G to initial, last or new seed.
• initialize.py initializes the random number library.
• initialized_get.py queries the INITIALIZED value.
• initialized_memory.py stores the INITIALIZED value.
• initialized_set.py sets the INITIALIZED value.
• lg_get.py queries the LG values for a given generator.
• lg_memory.py stores the LG values for all generators.
• lg_set.py sets the LG values for a given generator.
• multmod.py carries out modular multiplication.
• r4_uni_01.py returns a uniform random real number in [0,1].
• r8_uni_01.py returns a uniform random real number in [0,1].
• set_initial_seed.py resets the initial seed and state for all generators.
• set_seed.py resets the initial seed and the state of generator G.
• timestamp.py prints the current YMDHMS date as a time stamp.

Examples and Tests:

• rnglib_test.py, calls all the tests.
• rnglib_test.sh, runs all the tests.
• rnglib_test03.py, shows how the output of r4_uni_01() can be controlled by resetting the seed.
• rnglib_test04.py, shows how multiple random number generators can be used to create independent streams of data.
• rnglib_test.sh, a BASH shell script that runs the tests.
• rnglib_test.txt, the output file.

You can go up one level to the PYTHON source codes.