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abc.rst.txt	7219	0644	editdlrm
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atexit.rst.txt	3910	0644	editdlrm
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easydialogs.rst.txt	10346	0644	editdlrm
email-examples.rst.txt	1271	0644	editdlrm
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framework.rst.txt	11444	0644	editdlrm
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ftplib.rst.txt	15720	0644	editdlrm
functions.rst.txt	75554	0644	editdlrm
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future_builtins.rst.txt	2010	0644	editdlrm
gc.rst.txt	9014	0644	editdlrm
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hotshot.rst.txt	4289	0644	editdlrm
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httplib.rst.txt	37454	0644	editdlrm
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ic.rst.txt	5005	0644	editdlrm
idle.rst.txt	22182	0644	editdlrm
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io.rst.txt	39015	0644	editdlrm
ipc.rst.txt	630	0644	editdlrm
itertools.rst.txt	36324	0644	editdlrm
jpeg.rst.txt	3858	0644	editdlrm
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markup.rst.txt	1249	0644	editdlrm
marshal.rst.txt	5689	0644	editdlrm
math.rst.txt	10920	0644	editdlrm
md5.rst.txt	2815	0644	editdlrm
mhlib.rst.txt	3966	0644	editdlrm
mimetools.rst.txt	4504	0644	editdlrm
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mimewriter.rst.txt	3360	0644	editdlrm
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modulefinder.rst.txt	3380	0644	editdlrm
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msilib.rst.txt	19062	0644	editdlrm
msvcrt.rst.txt	4346	0644	editdlrm
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new.rst.txt	2653	0644	editdlrm
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numbers.rst.txt	8007	0644	editdlrm
numeric.rst.txt	751	0644	editdlrm
operator.rst.txt	22092	0644	editdlrm
optparse.rst.txt	77101	0644	editdlrm
os.path.rst.txt	13094	0644	editdlrm
os.rst.txt	82580	0644	editdlrm
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othergui.rst.txt	2721	0644	editdlrm
parser.rst.txt	15385	0644	editdlrm
pdb.rst.txt	16043	0644	editdlrm
persistence.rst.txt	826	0644	editdlrm
pickle.rst.txt	37262	0644	editdlrm
pickletools.rst.txt	1997	0644	editdlrm
pipes.rst.txt	3786	0644	editdlrm
pkgutil.rst.txt	7714	0644	editdlrm
platform.rst.txt	9554	0644	editdlrm
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popen2.rst.txt	7022	0644	editdlrm
poplib.rst.txt	6220	0644	editdlrm
posix.rst.txt	3630	0644	editdlrm
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pprint.rst.txt	9071	0644	editdlrm
profile.rst.txt	28666	0644	editdlrm
pty.rst.txt	1762	0644	editdlrm
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pyclbr.rst.txt	3296	0644	editdlrm
pydoc.rst.txt	4083	0644	editdlrm
pyexpat.rst.txt	28891	0644	editdlrm
python.rst.txt	514	0644	editdlrm
py_compile.rst.txt	2479	0644	editdlrm
queue.rst.txt	7034	0644	editdlrm
quopri.rst.txt	2670	0644	editdlrm
random.rst.txt	13319	0644	editdlrm
re.rst.txt	55102	0644	editdlrm
readline.rst.txt	10513	0644	editdlrm
repr.rst.txt	4718	0644	editdlrm
resource.rst.txt	9595	0644	editdlrm
restricted.rst.txt	3327	0644	editdlrm
rexec.rst.txt	11742	0644	editdlrm
rfc822.rst.txt	14037	0644	editdlrm
rlcompleter.rst.txt	2494	0644	editdlrm
robotparser.rst.txt	2190	0644	editdlrm
runpy.rst.txt	6938	0644	editdlrm
sched.rst.txt	4644	0644	editdlrm
scrolledtext.rst.txt	1379	0644	editdlrm
select.rst.txt	20705	0644	editdlrm
sets.rst.txt	14973	0644	editdlrm
sgi.rst.txt	322	0644	editdlrm
sgmllib.rst.txt	10664	0644	editdlrm
sha.rst.txt	2807	0644	editdlrm
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shlex.rst.txt	11313	0644	editdlrm
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signal.rst.txt	10719	0644	editdlrm
simplehttpserver.rst.txt	4559	0644	editdlrm
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site.rst.txt	7880	0644	editdlrm
smtpd.rst.txt	2464	0644	editdlrm
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sndhdr.rst.txt	1759	0644	editdlrm
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string.rst.txt	44085	0644	editdlrm
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:mod:`random` --- Generate pseudo-random numbers ================================================ .. module:: random :synopsis: Generate pseudo-random numbers with various common distributions. **Source code:** :source:`Lib/random.py` -------------- This module implements pseudo-random number generators for various distributions. For integers, uniform selection from a range. For sequences, uniform selection of a random element, a function to generate a random permutation of a list in-place, and a function for random sampling without replacement. On the real line, there are functions to compute uniform, normal (Gaussian), lognormal, negative exponential, gamma, and beta distributions. For generating distributions of angles, the von Mises distribution is available. Almost all module functions depend on the basic function :func:`.random`, which generates a random float uniformly in the semi-open range [0.0, 1.0). Python uses the Mersenne Twister as the core generator. It produces 53-bit precision floats and has a period of 2\*\*19937-1. The underlying implementation in C is both fast and threadsafe. The Mersenne Twister is one of the most extensively tested random number generators in existence. However, being completely deterministic, it is not suitable for all purposes, and is completely unsuitable for cryptographic purposes. The functions supplied by this module are actually bound methods of a hidden instance of the :class:`random.Random` class. You can instantiate your own instances of :class:`Random` to get generators that don't share state. This is especially useful for multi-threaded programs, creating a different instance of :class:`Random` for each thread, and using the :meth:`jumpahead` method to make it likely that the generated sequences seen by each thread don't overlap. Class :class:`Random` can also be subclassed if you want to use a different basic generator of your own devising: in that case, override the :meth:`~Random.random`, :meth:`~Random.seed`, :meth:`~Random.getstate`, :meth:`~Random.setstate` and :meth:`~Random.jumpahead` methods. Optionally, a new generator can supply a :meth:`~Random.getrandbits` method --- this allows :meth:`randrange` to produce selections over an arbitrarily large range. .. versionadded:: 2.4 the :meth:`getrandbits` method. As an example of subclassing, the :mod:`random` module provides the :class:`WichmannHill` class that implements an alternative generator in pure Python. The class provides a backward compatible way to reproduce results from earlier versions of Python, which used the Wichmann-Hill algorithm as the core generator. Note that this Wichmann-Hill generator can no longer be recommended: its period is too short by contemporary standards, and the sequence generated is known to fail some stringent randomness tests. See the references below for a recent variant that repairs these flaws. .. versionchanged:: 2.3 MersenneTwister replaced Wichmann-Hill as the default generator. The :mod:`random` module also provides the :class:`SystemRandom` class which uses the system function :func:`os.urandom` to generate random numbers from sources provided by the operating system. .. warning:: The pseudo-random generators of this module should not be used for security purposes. Use :func:`os.urandom` or :class:`SystemRandom` if you require a cryptographically secure pseudo-random number generator. Bookkeeping functions: .. function:: seed(a=None) Initialize internal state of the random number generator. ``None`` or no argument seeds from current time or from an operating system specific randomness source if available (see the :func:`os.urandom` function for details on availability). If *a* is not ``None`` or an :class:`int` or a :class:`long`, then ``hash(a)`` is used instead. Note that the hash values for some types are nondeterministic when :envvar:`PYTHONHASHSEED` is enabled. .. versionchanged:: 2.4 formerly, operating system resources were not used. .. function:: getstate() Return an object capturing the current internal state of the generator. This object can be passed to :func:`setstate` to restore the state. .. versionadded:: 2.1 .. versionchanged:: 2.6 State values produced in Python 2.6 cannot be loaded into earlier versions. .. function:: setstate(state) *state* should have been obtained from a previous call to :func:`getstate`, and :func:`setstate` restores the internal state of the generator to what it was at the time :func:`getstate` was called. .. versionadded:: 2.1 .. function:: jumpahead(n) Change the internal state to one different from and likely far away from the current state. *n* is a non-negative integer which is used to scramble the current state vector. This is most useful in multi-threaded programs, in conjunction with multiple instances of the :class:`Random` class: :meth:`setstate` or :meth:`seed` can be used to force all instances into the same internal state, and then :meth:`jumpahead` can be used to force the instances' states far apart. .. versionadded:: 2.1 .. versionchanged:: 2.3 Instead of jumping to a specific state, *n* steps ahead, ``jumpahead(n)`` jumps to another state likely to be separated by many steps. .. function:: getrandbits(k) Returns a python :class:`long` int with *k* random bits. This method is supplied with the MersenneTwister generator and some other generators may also provide it as an optional part of the API. When available, :meth:`getrandbits` enables :meth:`randrange` to handle arbitrarily large ranges. .. versionadded:: 2.4 Functions for integers: .. function:: randrange(stop) randrange(start, stop[, step]) Return a randomly selected element from ``range(start, stop, step)``. This is equivalent to ``choice(range(start, stop, step))``, but doesn't actually build a range object. .. versionadded:: 1.5.2 .. function:: randint(a, b) Return a random integer *N* such that ``a <= N <= b``. Functions for sequences: .. function:: choice(seq) Return a random element from the non-empty sequence *seq*. If *seq* is empty, raises :exc:`IndexError`. .. function:: shuffle(x[, random]) Shuffle the sequence *x* in place. The optional argument *random* is a 0-argument function returning a random float in [0.0, 1.0); by default, this is the function :func:`.random`. Note that for even rather small ``len(x)``, the total number of permutations of *x* is larger than the period of most random number generators; this implies that most permutations of a long sequence can never be generated. .. function:: sample(population, k) Return a *k* length list of unique elements chosen from the population sequence. Used for random sampling without replacement. .. versionadded:: 2.3 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 :term:`hashable` or unique. If the population contains repeats, then each occurrence is a possible selection in the sample. To choose a sample from a range of integers, use an :func:`xrange` object as an argument. This is especially fast and space efficient for sampling from a large population: ``sample(xrange(10000000), 60)``. The following functions generate specific real-valued distributions. Function parameters are named after the corresponding variables in the distribution's equation, as used in common mathematical practice; most of these equations can be found in any statistics text. .. function:: random() Return the next random floating point number in the range [0.0, 1.0). .. function:: uniform(a, b) Return a random floating point number *N* such that ``a <= N <= b`` for ``a <= b`` and ``b <= N <= a`` for ``b < a``. The end-point value ``b`` may or may not be included in the range depending on floating-point rounding in the equation ``a + (b-a) * random()``. .. function:: triangular(low, high, mode) Return a random floating point number *N* such that ``low <= N <= high`` and with the specified *mode* between those bounds. The *low* and *high* bounds default to zero and one. The *mode* argument defaults to the midpoint between the bounds, giving a symmetric distribution. .. versionadded:: 2.6 .. function:: betavariate(alpha, beta) Beta distribution. Conditions on the parameters are ``alpha > 0`` and ``beta > 0``. Returned values range between 0 and 1. .. function:: expovariate(lambd) Exponential distribution. *lambd* is 1.0 divided by the desired mean. It should be nonzero. (The parameter would be called "lambda", but that is a reserved word in Python.) Returned values range from 0 to positive infinity if *lambd* is positive, and from negative infinity to 0 if *lambd* is negative. .. function:: gammavariate(alpha, beta) Gamma distribution. (*Not* the gamma function!) Conditions on the parameters are ``alpha > 0`` and ``beta > 0``. The probability distribution function is:: x ** (alpha - 1) * math.exp(-x / beta) pdf(x) = -------------------------------------- math.gamma(alpha) * beta ** alpha .. function:: gauss(mu, sigma) Gaussian distribution. *mu* is the mean, and *sigma* is the standard deviation. This is slightly faster than the :func:`normalvariate` function defined below. .. function:: lognormvariate(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. .. function:: normalvariate(mu, sigma) Normal distribution. *mu* is the mean, and *sigma* is the standard deviation. .. function:: vonmisesvariate(mu, kappa) *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*. .. function:: paretovariate(alpha) Pareto distribution. *alpha* is the shape parameter. .. function:: weibullvariate(alpha, beta) Weibull distribution. *alpha* is the scale parameter and *beta* is the shape parameter. Alternative Generators: .. class:: WichmannHill([seed]) Class that implements the Wichmann-Hill algorithm as the core generator. Has all of the same methods as :class:`Random` plus the :meth:`whseed` method described below. Because this class is implemented in pure Python, it is not threadsafe and may require locks between calls. The period of the generator is 6,953,607,871,644 which is small enough to require care that two independent random sequences do not overlap. .. function:: whseed([x]) This is obsolete, supplied for bit-level compatibility with versions of Python prior to 2.1. See :func:`seed` for details. :func:`whseed` does not guarantee that distinct integer arguments yield distinct internal states, and can yield no more than about 2\*\*24 distinct internal states in all. .. class:: SystemRandom([seed]) Class that uses the :func:`os.urandom` function for generating random numbers from sources provided by the operating system. Not available on all systems. Does not rely on software state and sequences are not reproducible. Accordingly, the :meth:`seed` and :meth:`jumpahead` methods have no effect and are ignored. The :meth:`getstate` and :meth:`setstate` methods raise :exc:`NotImplementedError` if called. .. versionadded:: 2.4 Examples of basic usage:: >>> random.random() # Random float x, 0.0 <= x < 1.0 0.37444887175646646 >>> random.uniform(1, 10) # Random float x, 1.0 <= x < 10.0 1.1800146073117523 >>> random.randint(1, 10) # Integer from 1 to 10, endpoints included 7 >>> random.randrange(0, 101, 2) # Even integer from 0 to 100 26 >>> random.choice('abcdefghij') # Choose a random element 'c' >>> items = [1, 2, 3, 4, 5, 6, 7] >>> random.shuffle(items) >>> items [7, 3, 2, 5, 6, 4, 1] >>> random.sample([1, 2, 3, 4, 5], 3) # Choose 3 elements [4, 1, 5] .. seealso:: M. Matsumoto and T. Nishimura, "Mersenne Twister: A 623-dimensionally equidistributed uniform pseudorandom number generator", ACM Transactions on Modeling and Computer Simulation Vol. 8, No. 1, January pp.3--30 1998. Wichmann, B. A. & Hill, I. D., "Algorithm AS 183: An efficient and portable pseudo-random number generator", Applied Statistics 31 (1982) 188-190. `Complementary-Multiply-with-Carry recipe `_ for a compatible alternative random number generator with a long period and comparatively simple update operations.