{ "info": { "author": "R. Samadi", "author_email": "reza.samadi@obspm.fr", "bugtrack_url": null, "classifiers": [], "description": "PyMT64\n \nPyMT64 is a Python version of the Mersenne Twister (MT) 64-bit pseudorandom number generator by Takuji Nishimura and Makoto Matsumoto (see http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt64.html and the references below).\n\nThis customised version is thread safe and was interfaced from C to Python (see pymt64.c)\n\nThis module provides the following methods:\n- init : initialization of the state vector (mt) used by the pseudorandom number generator (PNG)\n- uniform : generation of an uniform distribution\n- normal : generation of two Normal distributions\n- poisson : generation of a Poisson distribution\n\nThe period of the PNG is 2**19937-1.\n\nExample:\n\timport time\n\timport pymt64\n\tseed = int(time.time()) # the initial seed\n\tmt = pymt64.init(seed) # initialisation of the state vector of MT\n\tu = pymt64.uniform(mt,10) # generation of an uniform distribution\n\tprint u\n\t[ 0.94295421 0.47222327 0.634552 0.26012686 0.38431784 0.23995444\n\t 0.02175826 0.8209848 0.79266556 0.8638286 ]\n\nFor a complete example, see pymt64_test.py\n\nNote: the state vector 'mt' returned by pymt64.init has 313 elements instead of the 312 elements of the original C code. This is because the 313th element store the associated counter (mti).\n\nChange history:\n\n1.3 : fixe a compilation problem regading the Numpy include directory\n1.2 : the previous implementation of the poisson distribution was not thread safe\n1.1 : fix a problem with the initialization of the seed (in the previous version the seed set by init() was not taken into account such that the results were not reproductible)\n1.0 : initial version\n\n\nR. Samadi (LESIA, Observatoire de Paris), 22 Dec. 2012\t \n\nReferences:\n T. Nishimura, ``Tables of 64-bit Mersenne Twisters''\n ACM Transactions on Modeling and \n Computer Simulation 10. (2000) 348--357.\n M. Matsumoto and T. Nishimura,\n ``Mersenne Twister: a 623-dimensionally equidistributed\n uniform pseudorandom number generator''\n ACM Transactions on Modeling and \n Computer Simulation 8. 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