diff -r ffa851df0825 -r 2fb8b9db1c86 symbian-qemu-0.9.1-12/python-win32-2.6.1/lib/random.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/symbian-qemu-0.9.1-12/python-win32-2.6.1/lib/random.py	Fri Jul 31 15:01:17 2009 +0100
@@ -0,0 +1,896 @@
+"""Random variable generators.
+
+    integers
+    --------
+           uniform within range
+
+    sequences
+    ---------
+           pick random element
+           pick random sample
+           generate random permutation
+
+    distributions on the real line:
+    ------------------------------
+           uniform
+           triangular
+           normal (Gaussian)
+           lognormal
+           negative exponential
+           gamma
+           beta
+           pareto
+           Weibull
+
+    distributions on the circle (angles 0 to 2pi)
+    ---------------------------------------------
+           circular uniform
+           von Mises
+
+General notes on the underlying Mersenne Twister core generator:
+
+* The period is 2**19937-1.
+* It is one of the most extensively tested generators in existence.
+* Without a direct way to compute N steps forward, the semantics of
+  jumpahead(n) are weakened to simply jump to another distant state and rely
+  on the large period to avoid overlapping sequences.
+* The random() method is implemented in C, executes in a single Python step,
+  and is, therefore, threadsafe.
+
+"""
+
+from __future__ import division
+from warnings import warn as _warn
+from types import MethodType as _MethodType, BuiltinMethodType as _BuiltinMethodType
+from math import log as _log, exp as _exp, pi as _pi, e as _e, ceil as _ceil
+from math import sqrt as _sqrt, acos as _acos, cos as _cos, sin as _sin
+from os import urandom as _urandom
+from binascii import hexlify as _hexlify
+
+__all__ = ["Random","seed","random","uniform","randint","choice","sample",
+           "randrange","shuffle","normalvariate","lognormvariate",
+           "expovariate","vonmisesvariate","gammavariate","triangular",
+           "gauss","betavariate","paretovariate","weibullvariate",
+           "getstate","setstate","jumpahead", "WichmannHill", "getrandbits",
+           "SystemRandom"]
+
+NV_MAGICCONST = 4 * _exp(-0.5)/_sqrt(2.0)
+TWOPI = 2.0*_pi
+LOG4 = _log(4.0)
+SG_MAGICCONST = 1.0 + _log(4.5)
+BPF = 53        # Number of bits in a float
+RECIP_BPF = 2**-BPF
+
+
+# Translated by Guido van Rossum from C source provided by
+# Adrian Baddeley.  Adapted by Raymond Hettinger for use with
+# the Mersenne Twister  and os.urandom() core generators.
+
+import _random
+
+class Random(_random.Random):
+    """Random number generator base class used by bound module functions.
+
+    Used to instantiate instances of Random to get generators that don't
+    share state.  Especially useful for multi-threaded programs, creating
+    a different instance of Random for each thread, and using the jumpahead()
+    method to ensure that the generated sequences seen by each thread don't
+    overlap.
+
+    Class Random can also be subclassed if you want to use a different basic
+    generator of your own devising: in that case, override the following
+    methods: random(), seed(), getstate(), setstate() and jumpahead().
+    Optionally, implement a getrandbits() method so that randrange() can cover
+    arbitrarily large ranges.
+
+    """
+
+    VERSION = 3     # used by getstate/setstate
+
+    def __init__(self, x=None):
+        """Initialize an instance.
+
+        Optional argument x controls seeding, as for Random.seed().
+        """
+
+        self.seed(x)
+        self.gauss_next = None
+
+    def seed(self, a=None):
+        """Initialize internal state from hashable object.
+
+        None or no argument seeds from current time or from an operating
+        system specific randomness source if available.
+
+        If a is not None or an int or long, hash(a) is used instead.
+        """
+
+        if a is None:
+            try:
+                a = long(_hexlify(_urandom(16)), 16)
+            except NotImplementedError:
+                import time
+                a = long(time.time() * 256) # use fractional seconds
+
+        super(Random, self).seed(a)
+        self.gauss_next = None
+
+    def getstate(self):
+        """Return internal state; can be passed to setstate() later."""
+        return self.VERSION, super(Random, self).getstate(), self.gauss_next
+
+    def setstate(self, state):
+        """Restore internal state from object returned by getstate()."""
+        version = state[0]
+        if version == 3:
+            version, internalstate, self.gauss_next = state
+            super(Random, self).setstate(internalstate)
+        elif version == 2:
+            version, internalstate, self.gauss_next = state
+            # In version 2, the state was saved as signed ints, which causes
+            #   inconsistencies between 32/64-bit systems. The state is
+            #   really unsigned 32-bit ints, so we convert negative ints from
+            #   version 2 to positive longs for version 3.
+            try:
+                internalstate = tuple( long(x) % (2**32) for x in internalstate )
+            except ValueError, e:
+                raise TypeError, e
+            super(Random, self).setstate(internalstate)
+        else:
+            raise ValueError("state with version %s passed to "
+                             "Random.setstate() of version %s" %
+                             (version, self.VERSION))
+
+## ---- Methods below this point do not need to be overridden when
+## ---- subclassing for the purpose of using a different core generator.
+
+## -------------------- pickle support  -------------------
+
+    def __getstate__(self): # for pickle
+        return self.getstate()
+
+    def __setstate__(self, state):  # for pickle
+        self.setstate(state)
+
+    def __reduce__(self):
+        return self.__class__, (), self.getstate()
+
+## -------------------- integer methods  -------------------
+
+    def randrange(self, start, stop=None, step=1, int=int, default=None,
+                  maxwidth=1L<<BPF):
+        """Choose a random item from range(start, stop[, step]).
+
+        This fixes the problem with randint() which includes the
+        endpoint; in Python this is usually not what you want.
+        Do not supply the 'int', 'default', and 'maxwidth' arguments.
+        """
+
+        # This code is a bit messy to make it fast for the
+        # common case while still doing adequate error checking.
+        istart = int(start)
+        if istart != start:
+            raise ValueError, "non-integer arg 1 for randrange()"
+        if stop is default:
+            if istart > 0:
+                if istart >= maxwidth:
+                    return self._randbelow(istart)
+                return int(self.random() * istart)
+            raise ValueError, "empty range for randrange()"
+
+        # stop argument supplied.
+        istop = int(stop)
+        if istop != stop:
+            raise ValueError, "non-integer stop for randrange()"
+        width = istop - istart
+        if step == 1 and width > 0:
+            # Note that
+            #     int(istart + self.random()*width)
+            # instead would be incorrect.  For example, consider istart
+            # = -2 and istop = 0.  Then the guts would be in
+            # -2.0 to 0.0 exclusive on both ends (ignoring that random()
+            # might return 0.0), and because int() truncates toward 0, the
+            # final result would be -1 or 0 (instead of -2 or -1).
+            #     istart + int(self.random()*width)
+            # would also be incorrect, for a subtler reason:  the RHS
+            # can return a long, and then randrange() would also return
+            # a long, but we're supposed to return an int (for backward
+            # compatibility).
+
+            if width >= maxwidth:
+                return int(istart + self._randbelow(width))
+            return int(istart + int(self.random()*width))
+        if step == 1:
+            raise ValueError, "empty range for randrange() (%d,%d, %d)" % (istart, istop, width)
+
+        # Non-unit step argument supplied.
+        istep = int(step)
+        if istep != step:
+            raise ValueError, "non-integer step for randrange()"
+        if istep > 0:
+            n = (width + istep - 1) // istep
+        elif istep < 0:
+            n = (width + istep + 1) // istep
+        else:
+            raise ValueError, "zero step for randrange()"
+
+        if n <= 0:
+            raise ValueError, "empty range for randrange()"
+
+        if n >= maxwidth:
+            return istart + istep*self._randbelow(n)
+        return istart + istep*int(self.random() * n)
+
+    def randint(self, a, b):
+        """Return random integer in range [a, b], including both end points.
+        """
+
+        return self.randrange(a, b+1)
+
+    def _randbelow(self, n, _log=_log, int=int, _maxwidth=1L<<BPF,
+                   _Method=_MethodType, _BuiltinMethod=_BuiltinMethodType):
+        """Return a random int in the range [0,n)
+
+        Handles the case where n has more bits than returned
+        by a single call to the underlying generator.
+        """
+
+        try:
+            getrandbits = self.getrandbits
+        except AttributeError:
+            pass
+        else:
+            # Only call self.getrandbits if the original random() builtin method
+            # has not been overridden or if a new getrandbits() was supplied.
+            # This assures that the two methods correspond.
+            if type(self.random) is _BuiltinMethod or type(getrandbits) is _Method:
+                k = int(1.00001 + _log(n-1, 2.0))   # 2**k > n-1 > 2**(k-2)
+                r = getrandbits(k)
+                while r >= n:
+                    r = getrandbits(k)
+                return r
+        if n >= _maxwidth:
+            _warn("Underlying random() generator does not supply \n"
+                "enough bits to choose from a population range this large")
+        return int(self.random() * n)
+
+## -------------------- sequence methods  -------------------
+
+    def choice(self, seq):
+        """Choose a random element from a non-empty sequence."""
+        return seq[int(self.random() * len(seq))]  # raises IndexError if seq is empty
+
+    def shuffle(self, x, random=None, int=int):
+        """x, random=random.random -> shuffle list x in place; return None.
+
+        Optional arg random is a 0-argument function returning a random
+        float in [0.0, 1.0); by default, the standard random.random.
+        """
+
+        if random is None:
+            random = self.random
+        for i in reversed(xrange(1, len(x))):
+            # pick an element in x[:i+1] with which to exchange x[i]
+            j = int(random() * (i+1))
+            x[i], x[j] = x[j], x[i]
+
+    def sample(self, population, k):
+        """Chooses k unique random elements from a population sequence.
+
+        Returns a new list containing elements from the population while
+        leaving the original population unchanged.  The resulting list is
+        in selection order so that all sub-slices will also be valid random
+        samples.  This allows raffle winners (the sample) to be partitioned
+        into grand prize and second place winners (the subslices).
+
+        Members of the population need not be hashable or unique.  If the
+        population contains repeats, then each occurrence is a possible
+        selection in the sample.
+
+        To choose a sample in a range of integers, use xrange as an argument.
+        This is especially fast and space efficient for sampling from a
+        large population:   sample(xrange(10000000), 60)
+        """
+
+        # XXX Although the documentation says `population` is "a sequence",
+        # XXX attempts are made to cater to any iterable with a __len__
+        # XXX method.  This has had mixed success.  Examples from both
+        # XXX sides:  sets work fine, and should become officially supported;
+        # XXX dicts are much harder, and have failed in various subtle
+        # XXX ways across attempts.  Support for mapping types should probably
+        # XXX be dropped (and users should pass mapping.keys() or .values()
+        # XXX explicitly).
+
+        # Sampling without replacement entails tracking either potential
+        # selections (the pool) in a list or previous selections in a set.
+
+        # When the number of selections is small compared to the
+        # population, then tracking selections is efficient, requiring
+        # only a small set and an occasional reselection.  For
+        # a larger number of selections, the pool tracking method is
+        # preferred since the list takes less space than the
+        # set and it doesn't suffer from frequent reselections.
+
+        n = len(population)
+        if not 0 <= k <= n:
+            raise ValueError, "sample larger than population"
+        random = self.random
+        _int = int
+        result = [None] * k
+        setsize = 21        # size of a small set minus size of an empty list
+        if k > 5:
+            setsize += 4 ** _ceil(_log(k * 3, 4)) # table size for big sets
+        if n <= setsize or hasattr(population, "keys"):
+            # An n-length list is smaller than a k-length set, or this is a
+            # mapping type so the other algorithm wouldn't work.
+            pool = list(population)
+            for i in xrange(k):         # invariant:  non-selected at [0,n-i)
+                j = _int(random() * (n-i))
+                result[i] = pool[j]
+                pool[j] = pool[n-i-1]   # move non-selected item into vacancy
+        else:
+            try:
+                selected = set()
+                selected_add = selected.add
+                for i in xrange(k):
+                    j = _int(random() * n)
+                    while j in selected:
+                        j = _int(random() * n)
+                    selected_add(j)
+                    result[i] = population[j]
+            except (TypeError, KeyError):   # handle (at least) sets
+                if isinstance(population, list):
+                    raise
+                return self.sample(tuple(population), k)
+        return result
+
+## -------------------- real-valued distributions  -------------------
+
+## -------------------- uniform distribution -------------------
+
+    def uniform(self, a, b):
+        """Get a random number in the range [a, b)."""
+        return a + (b-a) * self.random()
+
+## -------------------- triangular --------------------
+
+    def triangular(self, low=0.0, high=1.0, mode=None):
+        """Triangular distribution.
+
+        Continuous distribution bounded by given lower and upper limits,
+        and having a given mode value in-between.
+
+        http://en.wikipedia.org/wiki/Triangular_distribution
+
+        """
+        u = self.random()
+        c = 0.5 if mode is None else (mode - low) / (high - low)
+        if u > c:
+            u = 1.0 - u
+            c = 1.0 - c
+            low, high = high, low
+        return low + (high - low) * (u * c) ** 0.5
+
+## -------------------- normal distribution --------------------
+
+    def normalvariate(self, mu, sigma):
+        """Normal distribution.
+
+        mu is the mean, and sigma is the standard deviation.
+
+        """
+        # mu = mean, sigma = standard deviation
+
+        # Uses Kinderman and Monahan method. Reference: Kinderman,
+        # A.J. and Monahan, J.F., "Computer generation of random
+        # variables using the ratio of uniform deviates", ACM Trans
+        # Math Software, 3, (1977), pp257-260.
+
+        random = self.random
+        while 1:
+            u1 = random()
+            u2 = 1.0 - random()
+            z = NV_MAGICCONST*(u1-0.5)/u2
+            zz = z*z/4.0
+            if zz <= -_log(u2):
+                break
+        return mu + z*sigma
+
+## -------------------- lognormal distribution --------------------
+
+    def lognormvariate(self, mu, sigma):
+        """Log normal distribution.
+
+        If you take the natural logarithm of this distribution, you'll get a
+        normal distribution with mean mu and standard deviation sigma.
+        mu can have any value, and sigma must be greater than zero.
+
+        """
+        return _exp(self.normalvariate(mu, sigma))
+
+## -------------------- exponential distribution --------------------
+
+    def expovariate(self, lambd):
+        """Exponential distribution.
+
+        lambd is 1.0 divided by the desired mean.  (The parameter would be
+        called "lambda", but that is a reserved word in Python.)  Returned
+        values range from 0 to positive infinity.
+
+        """
+        # lambd: rate lambd = 1/mean
+        # ('lambda' is a Python reserved word)
+
+        random = self.random
+        u = random()
+        while u <= 1e-7:
+            u = random()
+        return -_log(u)/lambd
+
+## -------------------- von Mises distribution --------------------
+
+    def vonmisesvariate(self, mu, kappa):
+        """Circular data distribution.
+
+        mu is the mean angle, expressed in radians between 0 and 2*pi, and
+        kappa is the concentration parameter, which must be greater than or
+        equal to zero.  If kappa is equal to zero, this distribution reduces
+        to a uniform random angle over the range 0 to 2*pi.
+
+        """
+        # mu:    mean angle (in radians between 0 and 2*pi)
+        # kappa: concentration parameter kappa (>= 0)
+        # if kappa = 0 generate uniform random angle
+
+        # Based upon an algorithm published in: Fisher, N.I.,
+        # "Statistical Analysis of Circular Data", Cambridge
+        # University Press, 1993.
+
+        # Thanks to Magnus Kessler for a correction to the
+        # implementation of step 4.
+
+        random = self.random
+        if kappa <= 1e-6:
+            return TWOPI * random()
+
+        a = 1.0 + _sqrt(1.0 + 4.0 * kappa * kappa)
+        b = (a - _sqrt(2.0 * a))/(2.0 * kappa)
+        r = (1.0 + b * b)/(2.0 * b)
+
+        while 1:
+            u1 = random()
+
+            z = _cos(_pi * u1)
+            f = (1.0 + r * z)/(r + z)
+            c = kappa * (r - f)
+
+            u2 = random()
+
+            if u2 < c * (2.0 - c) or u2 <= c * _exp(1.0 - c):
+                break
+
+        u3 = random()
+        if u3 > 0.5:
+            theta = (mu % TWOPI) + _acos(f)
+        else:
+            theta = (mu % TWOPI) - _acos(f)
+
+        return theta
+
+## -------------------- gamma distribution --------------------
+
+    def gammavariate(self, alpha, beta):
+        """Gamma distribution.  Not the gamma function!
+
+        Conditions on the parameters are alpha > 0 and beta > 0.
+
+        """
+
+        # alpha > 0, beta > 0, mean is alpha*beta, variance is alpha*beta**2
+
+        # Warning: a few older sources define the gamma distribution in terms
+        # of alpha > -1.0
+        if alpha <= 0.0 or beta <= 0.0:
+            raise ValueError, 'gammavariate: alpha and beta must be > 0.0'
+
+        random = self.random
+        if alpha > 1.0:
+
+            # Uses R.C.H. Cheng, "The generation of Gamma
+            # variables with non-integral shape parameters",
+            # Applied Statistics, (1977), 26, No. 1, p71-74
+
+            ainv = _sqrt(2.0 * alpha - 1.0)
+            bbb = alpha - LOG4
+            ccc = alpha + ainv
+
+            while 1:
+                u1 = random()
+                if not 1e-7 < u1 < .9999999:
+                    continue
+                u2 = 1.0 - random()
+                v = _log(u1/(1.0-u1))/ainv
+                x = alpha*_exp(v)
+                z = u1*u1*u2
+                r = bbb+ccc*v-x
+                if r + SG_MAGICCONST - 4.5*z >= 0.0 or r >= _log(z):
+                    return x * beta
+
+        elif alpha == 1.0:
+            # expovariate(1)
+            u = random()
+            while u <= 1e-7:
+                u = random()
+            return -_log(u) * beta
+
+        else:   # alpha is between 0 and 1 (exclusive)
+
+            # Uses ALGORITHM GS of Statistical Computing - Kennedy & Gentle
+
+            while 1:
+                u = random()
+                b = (_e + alpha)/_e
+                p = b*u
+                if p <= 1.0:
+                    x = p ** (1.0/alpha)
+                else:
+                    x = -_log((b-p)/alpha)
+                u1 = random()
+                if p > 1.0:
+                    if u1 <= x ** (alpha - 1.0):
+                        break
+                elif u1 <= _exp(-x):
+                    break
+            return x * beta
+
+## -------------------- Gauss (faster alternative) --------------------
+
+    def gauss(self, mu, sigma):
+        """Gaussian distribution.
+
+        mu is the mean, and sigma is the standard deviation.  This is
+        slightly faster than the normalvariate() function.
+
+        Not thread-safe without a lock around calls.
+
+        """
+
+        # When x and y are two variables from [0, 1), uniformly
+        # distributed, then
+        #
+        #    cos(2*pi*x)*sqrt(-2*log(1-y))
+        #    sin(2*pi*x)*sqrt(-2*log(1-y))
+        #
+        # are two *independent* variables with normal distribution
+        # (mu = 0, sigma = 1).
+        # (Lambert Meertens)
+        # (corrected version; bug discovered by Mike Miller, fixed by LM)
+
+        # Multithreading note: When two threads call this function
+        # simultaneously, it is possible that they will receive the
+        # same return value.  The window is very small though.  To
+        # avoid this, you have to use a lock around all calls.  (I
+        # didn't want to slow this down in the serial case by using a
+        # lock here.)
+
+        random = self.random
+        z = self.gauss_next
+        self.gauss_next = None
+        if z is None:
+            x2pi = random() * TWOPI
+            g2rad = _sqrt(-2.0 * _log(1.0 - random()))
+            z = _cos(x2pi) * g2rad
+            self.gauss_next = _sin(x2pi) * g2rad
+
+        return mu + z*sigma
+
+## -------------------- beta --------------------
+## See
+## http://sourceforge.net/bugs/?func=detailbug&bug_id=130030&group_id=5470
+## for Ivan Frohne's insightful analysis of why the original implementation:
+##
+##    def betavariate(self, alpha, beta):
+##        # Discrete Event Simulation in C, pp 87-88.
+##
+##        y = self.expovariate(alpha)
+##        z = self.expovariate(1.0/beta)
+##        return z/(y+z)
+##
+## was dead wrong, and how it probably got that way.
+
+    def betavariate(self, alpha, beta):
+        """Beta distribution.
+
+        Conditions on the parameters are alpha > 0 and beta > 0.
+        Returned values range between 0 and 1.
+
+        """
+
+        # This version due to Janne Sinkkonen, and matches all the std
+        # texts (e.g., Knuth Vol 2 Ed 3 pg 134 "the beta distribution").
+        y = self.gammavariate(alpha, 1.)
+        if y == 0:
+            return 0.0
+        else:
+            return y / (y + self.gammavariate(beta, 1.))
+
+## -------------------- Pareto --------------------
+
+    def paretovariate(self, alpha):
+        """Pareto distribution.  alpha is the shape parameter."""
+        # Jain, pg. 495
+
+        u = 1.0 - self.random()
+        return 1.0 / pow(u, 1.0/alpha)
+
+## -------------------- Weibull --------------------
+
+    def weibullvariate(self, alpha, beta):
+        """Weibull distribution.
+
+        alpha is the scale parameter and beta is the shape parameter.
+
+        """
+        # Jain, pg. 499; bug fix courtesy Bill Arms
+
+        u = 1.0 - self.random()
+        return alpha * pow(-_log(u), 1.0/beta)
+
+## -------------------- Wichmann-Hill -------------------
+
+class WichmannHill(Random):
+
+    VERSION = 1     # used by getstate/setstate
+
+    def seed(self, a=None):
+        """Initialize internal state from hashable object.
+
+        None or no argument seeds from current time or from an operating
+        system specific randomness source if available.
+
+        If a is not None or an int or long, hash(a) is used instead.
+
+        If a is an int or long, a is used directly.  Distinct values between
+        0 and 27814431486575L inclusive are guaranteed to yield distinct
+        internal states (this guarantee is specific to the default
+        Wichmann-Hill generator).
+        """
+
+        if a is None:
+            try:
+                a = long(_hexlify(_urandom(16)), 16)
+            except NotImplementedError:
+                import time
+                a = long(time.time() * 256) # use fractional seconds
+
+        if not isinstance(a, (int, long)):
+            a = hash(a)
+
+        a, x = divmod(a, 30268)
+        a, y = divmod(a, 30306)
+        a, z = divmod(a, 30322)
+        self._seed = int(x)+1, int(y)+1, int(z)+1
+
+        self.gauss_next = None
+
+    def random(self):
+        """Get the next random number in the range [0.0, 1.0)."""
+
+        # Wichman-Hill random number generator.
+        #
+        # Wichmann, B. A. & Hill, I. D. (1982)
+        # Algorithm AS 183:
+        # An efficient and portable pseudo-random number generator
+        # Applied Statistics 31 (1982) 188-190
+        #
+        # see also:
+        #        Correction to Algorithm AS 183
+        #        Applied Statistics 33 (1984) 123
+        #
+        #        McLeod, A. I. (1985)
+        #        A remark on Algorithm AS 183
+        #        Applied Statistics 34 (1985),198-200
+
+        # This part is thread-unsafe:
+        # BEGIN CRITICAL SECTION
+        x, y, z = self._seed
+        x = (171 * x) % 30269
+        y = (172 * y) % 30307
+        z = (170 * z) % 30323
+        self._seed = x, y, z
+        # END CRITICAL SECTION
+
+        # Note:  on a platform using IEEE-754 double arithmetic, this can
+        # never return 0.0 (asserted by Tim; proof too long for a comment).
+        return (x/30269.0 + y/30307.0 + z/30323.0) % 1.0
+
+    def getstate(self):
+        """Return internal state; can be passed to setstate() later."""
+        return self.VERSION, self._seed, self.gauss_next
+
+    def setstate(self, state):
+        """Restore internal state from object returned by getstate()."""
+        version = state[0]
+        if version == 1:
+            version, self._seed, self.gauss_next = state
+        else:
+            raise ValueError("state with version %s passed to "
+                             "Random.setstate() of version %s" %
+                             (version, self.VERSION))
+
+    def jumpahead(self, n):
+        """Act as if n calls to random() were made, but quickly.
+
+        n is an int, greater than or equal to 0.
+
+        Example use:  If you have 2 threads and know that each will
+        consume no more than a million random numbers, create two Random
+        objects r1 and r2, then do
+            r2.setstate(r1.getstate())
+            r2.jumpahead(1000000)
+        Then r1 and r2 will use guaranteed-disjoint segments of the full
+        period.
+        """
+
+        if not n >= 0:
+            raise ValueError("n must be >= 0")
+        x, y, z = self._seed
+        x = int(x * pow(171, n, 30269)) % 30269
+        y = int(y * pow(172, n, 30307)) % 30307
+        z = int(z * pow(170, n, 30323)) % 30323
+        self._seed = x, y, z
+
+    def __whseed(self, x=0, y=0, z=0):
+        """Set the Wichmann-Hill seed from (x, y, z).
+
+        These must be integers in the range [0, 256).
+        """
+
+        if not type(x) == type(y) == type(z) == int:
+            raise TypeError('seeds must be integers')
+        if not (0 <= x < 256 and 0 <= y < 256 and 0 <= z < 256):
+            raise ValueError('seeds must be in range(0, 256)')
+        if 0 == x == y == z:
+            # Initialize from current time
+            import time
+            t = long(time.time() * 256)
+            t = int((t&0xffffff) ^ (t>>24))
+            t, x = divmod(t, 256)
+            t, y = divmod(t, 256)
+            t, z = divmod(t, 256)
+        # Zero is a poor seed, so substitute 1
+        self._seed = (x or 1, y or 1, z or 1)
+
+        self.gauss_next = None
+
+    def whseed(self, a=None):
+        """Seed from hashable object's hash code.
+
+        None or no argument seeds from current time.  It is not guaranteed
+        that objects with distinct hash codes lead to distinct internal
+        states.
+
+        This is obsolete, provided for compatibility with the seed routine
+        used prior to Python 2.1.  Use the .seed() method instead.
+        """
+
+        if a is None:
+            self.__whseed()
+            return
+        a = hash(a)
+        a, x = divmod(a, 256)
+        a, y = divmod(a, 256)
+        a, z = divmod(a, 256)
+        x = (x + a) % 256 or 1
+        y = (y + a) % 256 or 1
+        z = (z + a) % 256 or 1
+        self.__whseed(x, y, z)
+
+## --------------- Operating System Random Source  ------------------
+
+class SystemRandom(Random):
+    """Alternate random number generator using sources provided
+    by the operating system (such as /dev/urandom on Unix or
+    CryptGenRandom on Windows).
+
+     Not available on all systems (see os.urandom() for details).
+    """
+
+    def random(self):
+        """Get the next random number in the range [0.0, 1.0)."""
+        return (long(_hexlify(_urandom(7)), 16) >> 3) * RECIP_BPF
+
+    def getrandbits(self, k):
+        """getrandbits(k) -> x.  Generates a long int with k random bits."""
+        if k <= 0:
+            raise ValueError('number of bits must be greater than zero')
+        if k != int(k):
+            raise TypeError('number of bits should be an integer')
+        bytes = (k + 7) // 8                    # bits / 8 and rounded up
+        x = long(_hexlify(_urandom(bytes)), 16)
+        return x >> (bytes * 8 - k)             # trim excess bits
+
+    def _stub(self, *args, **kwds):
+        "Stub method.  Not used for a system random number generator."
+        return None
+    seed = jumpahead = _stub
+
+    def _notimplemented(self, *args, **kwds):
+        "Method should not be called for a system random number generator."
+        raise NotImplementedError('System entropy source does not have state.')
+    getstate = setstate = _notimplemented
+
+## -------------------- test program --------------------
+
+def _test_generator(n, func, args):
+    import time
+    print n, 'times', func.__name__
+    total = 0.0
+    sqsum = 0.0
+    smallest = 1e10
+    largest = -1e10
+    t0 = time.time()
+    for i in range(n):
+        x = func(*args)
+        total += x
+        sqsum = sqsum + x*x
+        smallest = min(x, smallest)
+        largest = max(x, largest)
+    t1 = time.time()
+    print round(t1-t0, 3), 'sec,',
+    avg = total/n
+    stddev = _sqrt(sqsum/n - avg*avg)
+    print 'avg %g, stddev %g, min %g, max %g' % \
+              (avg, stddev, smallest, largest)
+
+
+def _test(N=2000):
+    _test_generator(N, random, ())
+    _test_generator(N, normalvariate, (0.0, 1.0))
+    _test_generator(N, lognormvariate, (0.0, 1.0))
+    _test_generator(N, vonmisesvariate, (0.0, 1.0))
+    _test_generator(N, gammavariate, (0.01, 1.0))
+    _test_generator(N, gammavariate, (0.1, 1.0))
+    _test_generator(N, gammavariate, (0.1, 2.0))
+    _test_generator(N, gammavariate, (0.5, 1.0))
+    _test_generator(N, gammavariate, (0.9, 1.0))
+    _test_generator(N, gammavariate, (1.0, 1.0))
+    _test_generator(N, gammavariate, (2.0, 1.0))
+    _test_generator(N, gammavariate, (20.0, 1.0))
+    _test_generator(N, gammavariate, (200.0, 1.0))
+    _test_generator(N, gauss, (0.0, 1.0))
+    _test_generator(N, betavariate, (3.0, 3.0))
+    _test_generator(N, triangular, (0.0, 1.0, 1.0/3.0))
+
+# Create one instance, seeded from current time, and export its methods
+# as module-level functions.  The functions share state across all uses
+#(both in the user's code and in the Python libraries), but that's fine
+# for most programs and is easier for the casual user than making them
+# instantiate their own Random() instance.
+
+_inst = Random()
+seed = _inst.seed
+random = _inst.random
+uniform = _inst.uniform
+triangular = _inst.triangular
+randint = _inst.randint
+choice = _inst.choice
+randrange = _inst.randrange
+sample = _inst.sample
+shuffle = _inst.shuffle
+normalvariate = _inst.normalvariate
+lognormvariate = _inst.lognormvariate
+expovariate = _inst.expovariate
+vonmisesvariate = _inst.vonmisesvariate
+gammavariate = _inst.gammavariate
+gauss = _inst.gauss
+betavariate = _inst.betavariate
+paretovariate = _inst.paretovariate
+weibullvariate = _inst.weibullvariate
+getstate = _inst.getstate
+setstate = _inst.setstate
+jumpahead = _inst.jumpahead
+getrandbits = _inst.getrandbits
+
+if __name__ == '__main__':
+    _test()