OwlCyberSecurity - MANAGER
Edit File: random.cpython-33.pyc
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Z? e3 j@ Z@ e3 jA ZA e3 jB ZB e3 jC ZC e3 jD ZD e3 jE ZE e3 jF ZF e3 jG ZG e3 jH ZH eI d0 k r{e2 � n d( S(1 u� 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. * The random() method is implemented in C, executes in a single Python step, and is, therefore, threadsafe. i ( u warn( u MethodTypeu BuiltinMethodType( u logu expu piu eu ceil( u sqrtu acosu cosu sin( u urandom( u Setu Sequence( u sha512u Randomu seedu randomu uniformu randintu choiceu sampleu randrangeu shuffleu normalvariateu lognormvariateu expovariateu vonmisesvariateu gammavariateu triangularu gaussu betavariateu paretovariateu weibullvariateu getstateu setstateu getrandbitsu SystemRandomi g �?g @g @g �?g @i5 i Nc s� | Ee Z d Z d Z d Z d7 d d � Z d7 d � f d d � Z � f d d � Z � f d d � Z d d � Z d d � Z d d � Z d7 d e d d � Z d d � Z e d e >e e e d d � Z d d � Z d7 d d � Z d d � Z d d � Z d! d" d7 d# d$ � Z d% d& � Z d' d( � Z d) d* � Z d+ d, � Z d- d. � Z d/ d0 � Z d1 d2 � Z d3 d4 � Z! d5 d6 � Z" � S(8 u Randomu� Random number generator base class used by bound module functions. Used to instantiate instances of Random to get generators that don't share state. 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(), and setstate(). Optionally, implement a getrandbits() method so that randrange() can cover arbitrarily large ranges. i c C s | j | � d | _ d S( ue Initialize an instance. Optional argument x controls seeding, as for Random.seed(). N( u seedu Noneu gauss_next( u selfu x( ( u+ /opt/alt/python33/lib64/python3.3/random.pyu __init__T s u Random.__init__i c s� | d k rb y t j t d � d � } Wqb t k r^ d d l } t | j � d � } Yqb Xn | d k r� t | t t t f � r� t | t � r� | j � } n | t | � j � 7} t j | d � } q� n t � j | � d | _ d S( u� Initialize internal state from hashable object. None or no argument seeds from current time or from an operating system specific randomness source if available. For version 2 (the default), all of the bits are used if *a* is a str, bytes, or bytearray. For version 1, the hash() of *a* is used instead. If *a* is an int, all bits are used. i u bigi Ni i ( u Noneu intu from_bytesu _urandomu NotImplementedErroru timeu isinstanceu stru bytesu bytearrayu encodeu _sha512u digestu superu seedu gauss_next( u selfu au versionu time( u __class__( u+ /opt/alt/python33/lib64/python3.3/random.pyu seed] s u Random.seedc s | j t � j � | j f S( u9 Return internal state; can be passed to setstate() later.( u VERSIONu superu getstateu gauss_next( u self( u __class__( u+ /opt/alt/python33/lib64/python3.3/random.pyu getstate{ s u Random.getstatec s� | d } | d k r; | \ } } | _ t � j | � n� | d k r� | \ } } | _ y t d d � | D� � } Wn. t k r� } z t | � WYd d } ~ Xn Xt � j | � n t d | | j f � � d S( u: Restore internal state from object returned by getstate().i i i c s s | ] } | d Vq d S( i i Nl ( ( u .0u x( ( u+ /opt/alt/python33/lib64/python3.3/random.pyu <genexpr>� s u" Random.setstate.<locals>.<genexpr>Nu? state with version %s passed to Random.setstate() of version %s( u gauss_nextu superu setstateu tupleu ValueErroru TypeErroru VERSION( u selfu stateu versionu internalstateu e( u __class__( u+ /opt/alt/python33/lib64/python3.3/random.pyu setstate s u Random.setstatec C s | j � S( N( u getstate( u self( ( u+ /opt/alt/python33/lib64/python3.3/random.pyu __getstate__� s u Random.__getstate__c C s | j | � d S( N( u setstate( u selfu state( ( u+ /opt/alt/python33/lib64/python3.3/random.pyu __setstate__� s u Random.__setstate__c C s | j f | j � f S( N( u __class__u getstate( u self( ( u+ /opt/alt/python33/lib64/python3.3/random.pyu __reduce__� s u Random.__reduce__i c C s� | | � } | | k r' t d � � n | d k r[ | d k rL | j | � St d � � n | | � } | | k r� t d � � n | | } | d k r� | d k r� | | j | � S| d k r� t d | | | f � � n | | � } | | k rt d � � n | d k r%| | d | } n- | d k rF| | d | } n t d � � | d k rmt d � � n | | | j | � S( u� 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. u! non-integer arg 1 for randrange()i u empty range for randrange()u non-integer stop for randrange()i u' empty range for randrange() (%d,%d, %d)u non-integer step for randrange()u zero step for randrange()N( u ValueErroru Noneu _randbelow( u selfu startu stopu stepu _intu istartu istopu widthu istepu n( ( u+ /opt/alt/python33/lib64/python3.3/random.pyu randrange� s4 u Random.randrangec C s | j | | d � S( uJ Return random integer in range [a, b], including both end points. i ( u randrange( u selfu au b( ( u+ /opt/alt/python33/lib64/python3.3/random.pyu randint� s u Random.randintc C s� | j } | | j � | k s0 | | � | k rk | j � } | | � } x | | k rf | | � } qK W| S| j } | | k r� t d � | | � | � S| | } | | | } | � } x | | k r� | � } q� W| | | � | S( uC Return a random int in the range [0,n). Raises ValueError if n==0.u� Underlying random() generator does not supply enough bits to choose from a population range this large. To remove the range limitation, add a getrandbits() method.( u getrandbitsu randomu bit_lengthu _warn( u selfu nu intu maxsizeu typeu Methodu BuiltinMethodu getrandbitsu ku ru randomu remu limit( ( u+ /opt/alt/python33/lib64/python3.3/random.pyu _randbelow� s" ' u Random._randbelowc C sB y | j t | � � } Wn t k r9 t d � � Yn X| | S( u2 Choose a random element from a non-empty sequence.u$ Cannot choose from an empty sequence( u _randbelowu lenu ValueErroru IndexError( u selfu sequ i( ( u+ /opt/alt/python33/lib64/python3.3/random.pyu choice� s u Random.choicec C s� | d k rk | j } x� t t d t | � � � D]3 } | | d � } | | | | | | <| | <q1 Wn` t } xW t t d t | � � � D]: } | | � | d � } | | | | | | <| | <q� Wd S( u� 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. i N( u Noneu _randbelowu reversedu rangeu lenu int( u selfu xu randomu randbelowu iu ju _int( ( u+ /opt/alt/python33/lib64/python3.3/random.pyu shuffle� s "$"u Random.shufflec C s� t | t � r t | � } n t | t � s< t d � � n | j } t | � } d | k oh | k n s| t d � � n d g | } d } | d k r� | d t t | d d � � 7} n | | k r%t | � } x� t | � D]: } | | | � } | | | | <| | | d | | <q� Wnl t � } | j } xW t | � D]I } | | � } x | | k rt| | � } qYW| | � | | | | <qDW| S( u= Chooses k unique random elements from a population sequence or set. 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 range as an argument. This is especially fast and space efficient for sampling from a large population: sample(range(10000000), 60) u> Population must be a sequence or set. For dicts, use list(d).i u Sample larger than populationi i i i i N( u isinstanceu _Setu tupleu _Sequenceu TypeErroru _randbelowu lenu ValueErroru Noneu _ceilu _logu listu rangeu setu add( u selfu populationu ku randbelowu nu resultu setsizeu poolu iu ju selectedu selected_add( ( u+ /opt/alt/python33/lib64/python3.3/random.pyu sample s6 $ u Random.samplec C s | | | | j � S( uH Get a random number in the range [a, b) or [a, b] depending on rounding.( u random( u selfu au b( ( u+ /opt/alt/python33/lib64/python3.3/random.pyu uniformP s u Random.uniformg g �?c C sx | j � } | d k r d n | | | | } | | k r` d | } d | } | | } } n | | | | | d S( u� 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 g �?g �?N( u randomu None( u selfu lowu highu modeu uu c( ( u+ /opt/alt/python33/lib64/python3.3/random.pyu triangularV s $ u Random.triangularc C sh | j } xP | � } d | � } t | d | } | | d } | t | � k r Pq q | | | S( u\ Normal distribution. mu is the mean, and sigma is the standard deviation. g �?g �?g @( u randomu NV_MAGICCONSTu _log( u selfu muu sigmau randomu u1u u2u zu zz( ( u+ /opt/alt/python33/lib64/python3.3/random.pyu normalvariatei s u Random.normalvariatec C s t | j | | � � S( u� 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. ( u _expu normalvariate( u selfu muu sigma( ( u+ /opt/alt/python33/lib64/python3.3/random.pyu lognormvariate� s u Random.lognormvariatec C s t d | j � � | S( u^ 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. g �?( u _logu random( u selfu lambd( ( u+ /opt/alt/python33/lib64/python3.3/random.pyu expovariate� s u Random.expovariatec C s | j } | d k r t | � Sd | } | t d | | � } xe | � } t t | � } | | | } | � } | d | | k s� | d | t | � k rE PqE qE d | } | | d | | } | � } | d k r� | t | � t } n | t | � t } | S( uF 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. g���ư>g �?g �?( u randomu TWOPIu _sqrtu _cosu _piu _expu _acos( u selfu muu kappau randomu su ru u1u zu du u2u qu fu u3u theta( ( u+ /opt/alt/python33/lib64/python3.3/random.pyu vonmisesvariate� s& . u Random.vonmisesvariatec C s | d k s | d k r' t d � � n | j } | d k r t d | d � } | t } | | } x�| � } d | k o� d k n s� qg n d | � } t | d | � | } | t | � } | | | } | | | | } | t d | d k s| t | � k rg | | Sqg n� | d k r_| � } x | d k rO| � } q7Wt | � | Sx� | � } t | t } | | } | d k r�| d | } n t | | | � } | � } | d k r�| | | d k r�Pq�qb| t | � k rbPqbqb| | Sd S( uZ 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 g u* gammavariate: alpha and beta must be > 0.0g �?g @gH�����z>g�P���?g @N( u ValueErroru randomu _sqrtu LOG4u _logu _expu SG_MAGICCONSTu _e( u selfu alphau betau randomu ainvu bbbu cccu u1u u2u vu xu zu ru uu bu p( ( u+ /opt/alt/python33/lib64/python3.3/random.pyu gammavariate� sJ * u Random.gammavariatec C s� | j } | j } d | _ | d k rw | � t } t d t d | � � � } t | � | } t | � | | _ n | | | S( u� 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. g @g �?Ng �( u randomu gauss_nextu Noneu TWOPIu _sqrtu _logu _cosu _sin( u selfu muu sigmau randomu zu x2piu g2rad( ( u+ /opt/alt/python33/lib64/python3.3/random.pyu gauss s u Random.gaussc C s> | j | d � } | d k r" d S| | | j | d � Sd S( u� Beta distribution. Conditions on the parameters are alpha > 0 and beta > 0. Returned values range between 0 and 1. g �?i g N( u gammavariate( u selfu alphau betau y( ( u+ /opt/alt/python33/lib64/python3.3/random.pyu betavariateN s u Random.betavariatec C s d | j � } d | d | S( u3 Pareto distribution. alpha is the shape parameter.g �?( u random( u selfu alphau u( ( u+ /opt/alt/python33/lib64/python3.3/random.pyu paretovariate` s u Random.paretovariatec C s' d | j � } | t | � d | S( uf Weibull distribution. alpha is the scale parameter and beta is the shape parameter. g �?( u randomu _log( u selfu alphau betau u( ( u+ /opt/alt/python33/lib64/python3.3/random.pyu weibullvariatei s u Random.weibullvariateN(# u __name__u __module__u __qualname__u __doc__u VERSIONu Noneu __init__u seedu getstateu setstateu __getstate__u __setstate__u __reduce__u intu randrangeu randintu BPFu typeu _MethodTypeu _BuiltinMethodTypeu _randbelowu choiceu shuffleu sampleu uniformu triangularu normalvariateu lognormvariateu expovariateu vonmisesvariateu gammavariateu gaussu betavariateu paretovariateu weibullvariate( u __locals__( ( u __class__u+ /opt/alt/python33/lib64/python3.3/random.pyu RandomD s6 , > 0 H 5 c B sT | Ee Z d Z d Z d d � Z d d � Z d d � Z d d � Z e Z Z d S( u SystemRandomu� 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). c C s t j t d � d � d ?t S( u3 Get the next random number in the range [0.0, 1.0).i u bigi ( u intu from_bytesu _urandomu RECIP_BPF( u self( ( u+ /opt/alt/python33/lib64/python3.3/random.pyu random~ s u SystemRandom.randomc C sr | d k r t d � � n | t | � k r< t d � � n | d d } t j t | � d � } | | d | ?S( u: getrandbits(k) -> x. Generates an int with k random bits.i u( number of bits must be greater than zerou# number of bits should be an integeri i u big( u ValueErroru intu TypeErroru from_bytesu _urandom( u selfu ku numbytesu x( ( u+ /opt/alt/python33/lib64/python3.3/random.pyu getrandbits� s u SystemRandom.getrandbitsc O s d S( u< Stub method. Not used for a system random number generator.N( u None( u selfu argsu kwds( ( u+ /opt/alt/python33/lib64/python3.3/random.pyu seed� s u SystemRandom.seedc O s t d � � d S( uA Method should not be called for a system random number generator.u* System entropy source does not have state.N( u NotImplementedError( u selfu argsu kwds( ( u+ /opt/alt/python33/lib64/python3.3/random.pyu _notimplemented� s u SystemRandom._notimplementedN( u __name__u __module__u __qualname__u __doc__u randomu getrandbitsu seedu _notimplementedu getstateu setstate( u __locals__( ( u+ /opt/alt/python33/lib64/python3.3/random.pyu SystemRandomv s c C s d d l } t | d | j � d } d } d } d } | j � } xV t | � D]H } | | � } | | 7} | | | } t | | � } t | | � } qP W| j � } t t | | d � d d d �| | } t | | | | � } t d | | | | f � d S( Ni u timesg g _�Bi u sec,u endu u! avg %g, stddev %g, min %g, max %gg _��( u timeu printu __name__u rangeu minu maxu roundu _sqrt( u nu funcu argsu timeu totalu sqsumu smallestu largestu t0u iu xu t1u avgu stddev( ( u+ /opt/alt/python33/lib64/python3.3/random.pyu _test_generator� s& u _test_generatori� c C s t | t f � t | t d � t | t d � t | t d � t | t d � t | t d � t | t d � t | t d � t | t d � t | t d � t | t d � t | t d � t | t d � t | t d � t | t d � t | t d � d S( Ng g �?g{�G�z�?g�������?g @g �?g�������?g 4@g i@g @( g g �?( g g �?( g g �?( g{�G�z�?g �?( g�������?g �?( g�������?g @( g �?g �?( g�������?g �?( g �?g �?( g @g �?( g 4@g �?( g i@g �?( g g �?( g @g @gUUUUUU�?( g g �?gUUUUUU�?( u _test_generatoru randomu normalvariateu lognormvariateu vonmisesvariateu gammavariateu gaussu betavariateu triangular( u N( ( u+ /opt/alt/python33/lib64/python3.3/random.pyu _test� s u _testu __main__(J u __doc__u warningsu warnu _warnu typesu MethodTypeu _MethodTypeu BuiltinMethodTypeu _BuiltinMethodTypeu mathu logu _logu expu _expu piu _piu eu _eu ceilu _ceilu sqrtu _sqrtu acosu _acosu cosu _cosu sinu _sinu osu urandomu _urandomu collections.abcu Setu _Setu Sequenceu _Sequenceu hashlibu sha512u _sha512u __all__u NV_MAGICCONSTu TWOPIu LOG4u SG_MAGICCONSTu BPFu RECIP_BPFu _randomu Randomu SystemRandomu _test_generatoru _testu _instu seedu randomu uniformu triangularu randintu choiceu randrangeu sampleu shuffleu normalvariateu lognormvariateu expovariateu vonmisesvariateu gammavariateu gaussu betavariateu paretovariateu weibullvariateu getstateu setstateu getrandbitsu __name__( ( ( u+ /opt/alt/python33/lib64/python3.3/random.pyu <module>% sd (" � � 4!