Ë ‚Ö¦i�‡ãó^—dZddlmZddlmZmZm Z m Z m ZddlmZmZmZmZddlmZmZmZddlmZmZ m!Z"ddl#m$Z%ddl&m'Z(dd l)m*Z+dd l,m-Z.m/Z0dd l1m1Z2dd l#Z3dd l4Z4 dd l5m6Z7gd¢Z:ded«zed«z Z;ed«Zde> zZ?dZ@Gd„de4j‚«ZAGd„deA«ZBeA«ZCeCjˆZDeCjŠZEeCjŒZFeCjŽZGeCj�ZHeCj’ZIeCj”ZJeCj–ZKeCj˜ZLeCjšZMeCjœZNeCjžZOeCj ZPeCj¢ZQeCj¤ZReCj¦ZSeCj¨ZTeCjªZUeCj¬ZVeCj®ZWeCj°ZXeCj²ZYeCj´ZZeCj¶Z[d„Z\d!d„Z]e^e3d«re3j¾eCjˆ¬«e`d k(re]«y y #e8$r dd l9m6Z7Y�ŒÅwxYw)"aERandom variable generators. bytes ----- uniform bytes (values between 0 and 255) integers -------- uniform within range sequences --------- pick random element pick random sample pick weighted 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 discrete distributions ---------------------- binomial 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. é)Úwarn)ÚlogÚexpÚpiÚeÚceil)ÚsqrtÚacosÚcosÚsin)ÚtauÚfloorÚisfinite)ÚlgammaÚfabsÚlog2)Úurandom)ÚSequence)Úindex)Ú accumulateÚrepeat)ÚbisectN)Úsha512)ÚRandomÚ SystemRandomÚ betavariateÚbinomialvariateÚchoiceÚchoicesÚ expovariateÚ gammavariateÚgaussÚ getrandbitsÚgetstateÚlognormvariateÚ normalvariateÚ paretovariateÚ randbytesÚrandintÚrandomÚ randrangeÚsampleÚseedÚsetstateÚshuffleÚ triangularÚuniformÚvonmisesvariateÚweibullvariateégà¿ç@ç@çð?ç@é5éécó‡—eZdZdZdZd$d„Zd%ˆfd„ Zˆfd„Zˆfd„Zd„Z d „Z d „Z d „Z d „Z d ezfd„Ze Zd„Zdefd„Zd„Zd„Zd„Zddœd„Zd$dd dœd„Zd„Zd&d„Zd'd„Zd'd„Zd„Zd(d„Zd„Zd„Z d „Z!d!„Z"d"„Z#d)d#„Z$ˆxZ%S)*raãRandom number generator base class used by bound module functions. Used to instantiate instances of Random to get generators that don't share state. Class Random can also be subclassed if you want to use a different basic generator of your own devising: in that case, override the following methods: random(), seed(), getstate(), and setstate(). Optionally, implement a getrandbits() method so that randrange() can cover arbitrarily large ranges. éNcó4—|j|«d|_y)zeInitialize an instance. Optional argument x controls seeding, as for Random.seed(). N)r-Ú gauss_next)ÚselfÚxs ú/usr/lib64/python3.12/random.pyÚ__init__zRandom.__init__~s€ð � ‰ �!Œ ؈�óc 󜕗|dk(r†t|ttf«rpt|t«r|jd«n|}|rt |d«dznd}t t|«D] }d|z|z dz}Œ|t |«z}|dk(rdn|}nª|d k(rkt|tttf«rPt|t«r|j«}tj|t|«j«z«}n:t|td «tttttf«s td «‚t ‰|�E|«d |_y ) a\Initialize internal state from a seed. The only supported seed types are None, int, float, str, bytes, and bytearray. None or no argument seeds from current time or from an operating system specific randomness source if available. If *a* is an int, all bits are used. For version 2 (the default), all of the bits are used if *a* is a str, bytes, or bytearray. For version 1 (provided for reproducing random sequences from older versions of Python), the algorithm for str and bytes generates a narrower range of seeds. r;zlatin-1réiCBlÿÿÿÿéÿÿÿÿéþÿÿÿr:NzOThe only supported seed types are: None, int, float, str, bytes, and bytearray.)Ú isinstanceÚstrÚbytesÚdecodeÚordÚmapÚlenÚ bytearrayÚencodeÚintÚ from_bytesÚ_sha512ÚdigestÚtypeÚfloatÚ TypeErrorÚsuperr-r?)r@ÚaÚversionrAÚcÚ __class__s €rBr-z Random.seed‡sø€ð$ �aŠ<œJ q¬3´¨,Ô7Ü'1°!´UÔ';�—‘˜Ô#ÀˆAÙ"#”�A�a‘D“ ˜Q’¨ˆAÜœ˜a–[�Ø ‘k QÑ&Ð*<Ñ<‘ð!à ”�Q“‰KˆAؘ2’g‘ 1‰Aà ˜Š\œj¨¬S´%¼Ð,CÔDܘ!œSÔ!Ø—H‘H“J�Ü—‘˜q¤7¨1£:×#4Ñ#4Ó#6Ñ6Ó7‰Aä˜A¤ T£ ¬C´¼¼UÄIÐNÔOÜðEóFð Fô ‰‰ �QŒØˆ�rDcóN•—|jt‰|� «|jfS)z9Return internal state; can be passed to setstate() later.)ÚVERSIONrYr$r?)r@r]s €rBr$zRandom.getstate­s ø€à�|‰|œU™WÑ-Ó/°·±Ð@Ð@rDcó•—|d}|dk(r|\}}|_t‰|� |«y|dk(r.|\}}|_ td„|D««}t‰|� |«yt d|›d|j ›�«‚#t$r }t |‚d}~wwxYw)z:Restore internal state from object returned by getstate().rr=r:c3ó&K—|] }|dz–—Œ y­w)lN©)Ú.0rAs rBÚ z"Random.setstate..¾sèø€Ð%K¹]¸ a¨7¥m¹]ùs‚Nzstate with version z( passed to Random.setstate() of version )r?rYr.ÚtupleÚ ValueErrorrXr_)r@Ústater[Ú internalstaterr]s €rBr.zRandom.setstate±sœø€à˜‘(ˆØ �aŠ<Ø6;Ñ 3ˆG�] D¤OÜ ‰GÑ ˜]Õ +Ø ˜Š\Ø6;Ñ 3ˆG�] D¤Oð  'Ü %Ñ%K¹]Ó%KÓ K� ô ‰GÑ ˜]Õ +åâ% t§|¢|ð5ó6ð 6øô ò 'Ü QÐ&ûð 'ús¸A5Á5 B Á>BÂB có"—|j«S©N)r$©r@s rBÚ __getstate__zRandom.__getstate__Òs€Ø�}‰}‹ÐrDcó&—|j|«yrj)r.)r@rgs rBÚ __setstate__zRandom.__setstate__Õs€Ø � ‰ �eÕrDcó<—|jd|j«fS)Nrb)r]r$rks rBÚ __reduce__zRandom.__reduce__Øs€Ø�~‰~˜r 4§=¡=£?Ð2Ð2rDc óÊ—|jD]T}d|jvryd|jvr|j|_yd|jvsŒC|j|_yy)aControl how subclasses generate random integers. The algorithm a subclass can use depends on the random() and/or getrandbits() implementation available to it and determines whether it can generate random integers from arbitrarily large ranges. Ú _randbelowr#r*N)Ú__mro__Ú__dict__Ú_randbelow_with_getrandbitsrrÚ_randbelow_without_getrandbits)ÚclsÚkwargsr\s rBÚ__init_subclass__zRandom.__init_subclass__ÞsY€ð—”ˆAؘqŸz™zÑ)áØ § ¡ Ñ*Ø!$×!@Ñ!@�”ÙØ˜1Ÿ:™:Ò%Ø!$×!CÑ!C�”ÙñrDcót—|j}|j«}||«}||k\r||«}||k\rŒ|S)z;Return a random int in the range [0,n). Defined for n > 0.)r#Ú bit_length)r@Únr#ÚkÚrs rBruz"Random._randbelow_with_getrandbitsòsA€ð×&Ñ&ˆ Ø �L‰L‹NˆÙ ˜‹NˆØ�1ŠfÙ˜A“ˆAð�1‹fàˆrDr;cóΗ|j}||k\rtd«t|«|z«S||z}||z |z }|«}||k\r |«}||k\rŒ t||z«|zS)z‹Return a random int in the range [0,n). Defined for n > 0. The implementation does not use getrandbits, but only random. z¤Underlying random() generator does not supply enough bits to choose from a population range this large. To remove the range limitation, add a getrandbits() method.)r*Ú_warnÚ_floor)r@r|Úmaxsizer*ÚremÚlimitr~s rBrvz%Random._randbelow_without_getrandbitsüs€ð —‘ˆØ �Š<Ü ðNô Oô™&›( Q™,Ó'Ð 'ؘ‰kˆØ˜3‘ 'Ñ)ˆÙ ‹HˆØ�5ŠjÙ“ˆAð�5‹jä�a˜'‘kÓ" QÑ&Ð&rDcóJ—|j|dz«j|d«S)úGenerate n random bytes.éÚlittle)r#Úto_bytes©r@r|s rBr(zRandom.randbytess$€à×Ñ  A¡Ó&×/Ñ/°°8Ó<Ðt |«} t|«D]#}| ||z «}| || |<| ||z d z | |<Œ%| St«}|j }t|«D]+}| |«}||vr | |«}||vrŒ ||«||| |<Œ-| Scc} w)afChooses 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. Repeated elements can be specified one at a time or with the optional counts parameter. For example: sample(['red', 'blue'], counts=[4, 2], k=5) is equivalent to: sample(['red', 'red', 'red', 'red', 'blue', 'blue'], k=5) To choose a sample from a range of integers, use range() for the population argument. This is especially fast and space efficient for sampling from a large population: sample(range(10000000), 60) zAPopulation must be a sequence. For dicts or sets, use sorted(d).Nz2The number of counts does not match the populationrzCounts must be integerszCounts must be non-negative)r}z,Sample larger than population or is negativeéér4r=r;)rIÚ _SequencerXrOÚlistÚ _accumulaterfÚpoprRr,ržÚ_bisectrrÚ_ceilÚ_logÚsetÚadd)r@Ú populationr}r¢r|Ú cum_countsÚtotalÚ selectionsrÚsrŸÚresultÚsetsizeÚpoolr r¡ÚselectedÚ selected_adds rBr,z Random.samplegsð€ôj˜*¤iÔ0Üð@óAð Aä � ‹OˆØ Ð Üœk¨&Ó1Ó2ˆJÜ�:‹ !Ò#Ü Ð!UÓVÐVÙ(2�J—N‘NÔ$¸ˆEܘe¤SÔ)ÜÐ 9Ó:Ð:Ø�qŠyÜ Ð!>Ó?Ð?ØŸ™¤U¨5£\°Q˜Ó7ˆJ܈FÙ?IÓJ¹z¸!�J™v j°!Ó4Ó5¸zÑJÐ JØ—O‘Oˆ Ø�AŒ{˜Š{ÜÐKÓLÐ LðÜÐKÓLÐ LØ�˜!‘ˆØˆØ ˆqŠ5Ø �qœE¤$ q¨1¡u¨a£.Ó1Ñ1Ñ 1ˆGØ �Š<ô˜ Ó#ˆDܘ1–X�Ù˜a !™eÓ$�Ø  ™G��q‘ ؘq 1™u q™y™/��Q’ððˆ ô“uˆHØ#Ÿ<™<ˆLܘ1–X�Ù˜a“L�ؘ8‘mÙ! !› �Að˜8’má˜Q”Ø& q™M��q’ ð ð ˆ ùò3KsÂ;F>)Ú cum_weightsr}c ó\—|j}t|«}|€N|€6t}|dz }td|«D�cgc]}|||«|z«‘Œc}S t t |««}n |� t d«‚t|«|k7r td«‚|ddz} | dkr td«‚t| «s td«‚t} |d z } td|«D�cgc]}|| ||«| zd | «‘Œc}Scc}w#t $r#t|t«s‚|}t d|›�«d‚wxYwcc}w) zÑReturn a k sized list of population elements chosen with replacement. If the relative weights or cumulative weights are not specified, the selections are made with equal probability. Nçz4The number of choices must be a keyword argument: k=z2Cannot specify both weights and cumulative weightsz3The number of weights does not match the populationrGz*Total of weights must be greater than zerozTotal of weights must be finiter;r) r*rOr�Ú_repeatr§r¨rXrIrRrfÚ _isfiniterª) r@r¯Úweightsr¹r}r*r|rr r±rÚhis rBrzRandom.choicesÆsf€ð—‘ˆÜ � ‹OˆØ Р؈Ü�Ø�S‘�ÜAHÈÈqÔAQÓRÑAQ¸A˜ ¡5©«°A©Ó#6Ó7ÐAQÑRÐRð Ü"¤;¨wÓ#7Ó8‘ ðÐ ÜÐPÓQÐ QÜ ˆ{Ó ˜qÒ ÜÐRÓSÐ SؘB‘ #Ñ%ˆØ �CŠ<ÜÐIÓJÐ JܘÔÜÐ>Ó?Ð ?ÜˆØ �‰Uˆä   qÔ)ó+Ù)�Að™6 +©v«x¸%Ñ/?ÀÀBÓGÓHØ)ñ+ð +ùò+Søôò Ü! '¬3Ô/ØØ�ÜØKÈÈÐMóàðð  üò$+sµC5ÁC:ÃD)Ã:,D&có4—|||z |j«zzS)zåGet a random number in the range [a, b) or [a, b] depending on rounding. The mean (expected value) and variance of the random variable are: E[X] = (a + b) / 2 Var[X] = (b - a) ** 2 / 12 ©r*r—s rBr1zRandom.uniformîs€ð�A˜‘E˜TŸ[™[›]Ñ*Ñ*Ð*rDcó¸—|j«} |€dn ||z ||z z }||kDrd|z }d|z }||}}|||z t||z«zzS#t$r|cYSwxYw)a­Triangular distribution. Continuous distribution bounded by given lower and upper limits, and having a given mode value in-between. http://en.wikipedia.org/wiki/Triangular_distribution The mean (expected value) and variance of the random variable are: E[X] = (low + high + mode) / 3 Var[X] = (low**2 + high**2 + mode**2 - low*high - low*mode - high*mode) / 18 çà?r7)r*ÚZeroDivisionErrorÚ_sqrt)r@ÚlowÚhighÚmodeÚur\s rBr0zRandom.triangularùs‚€ð �K‰K‹Mˆð Ø�|‘¨$°©*¸À¹Ñ)DˆAð ˆqŠ5Ø�a‘ˆAØ�a‘ˆAؘc�ˆCØ�d˜S‘j¤E¨!¨a©%£LÑ0Ñ0Ð0øô !ò ØŠJð ús’A Á AÁAcóž—|j} |«}d|«z }t|dz z|z }||zdz }|t|« krnŒ9|||zzS)z\Normal distribution. mu is the mean, and sigma is the standard deviation. r7rÃr6)r*Ú NV_MAGICCONSTr¬)r@ÚmuÚsigmar*Úu1Úu2ÚzÚzzs rBr&zRandom.normalvariatesg€ð—‘ˆØÙ“ˆBØ‘v“x‘ˆBÜ  c¡Ñ*¨RÑ/ˆAØ�Q‘˜‘ˆBØ”d˜2“h�YŠØð ð�A˜‘I‰~ÐrDcóð—|j}|j}d|_|€N|«tz}tdt d|«z «z«}t |«|z}t |«|z|_|||zzS)zØGaussian 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. NgÀr7)r*r?ÚTWOPIrÅr¬Ú_cosÚ_sin)r@rÌrÍr*rÐÚx2piÚg2rads rBr"z Random.gauss'st€ð6—‘ˆØ �O‰OˆØˆŒØ ˆ9Ù“8œeÑ#ˆDܘ$¤ c©F«H¡nÓ!5Ñ5Ó6ˆEÜ�T“ ˜UÑ"ˆAÜ" 4›j¨5Ñ0ˆDŒOà�A˜‘I‰~ÐrDcó8—t|j||««S)zûLog normal distribution. If you take the natural logarithm of this distribution, you'll get a normal distribution with mean mu and standard deviation sigma. mu can have any value, and sigma must be greater than zero. )Ú_expr&)r@rÌrÍs rBr%zRandom.lognormvariateMs€ô�D×&Ñ& r¨5Ó1Ó2Ð2rDcóB—td|j«z « |z S)aìExponential distribution. lambd is 1.0 divided by the desired mean. It should be nonzero. (The parameter would be called "lambda", but that is a reserved word in Python.) Returned values range from 0 to positive infinity if lambd is positive, and from negative infinity to 0 if lambd is negative. The mean (expected value) and variance of the random variable are: E[X] = 1 / lambd Var[X] = 1 / lambd ** 2 r7)r¬r*)r@Úlambds rBr zRandom.expovariateWs"€ô$�S˜4Ÿ;™;›=Ñ(Ó)Ð)¨EÑ1Ð1rDcó —|j}|dkrt|«zSd|z }|td||zz«z} |«}tt|z«}|||zz }|«} | d||zz ks| d|z t |«zkrnŒId|z } | |zd| |zzz } |«} | dkDr|t | «ztz} | S|t | «z tz} | S)aFCircular 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�íµ ÷ư>rÃr7)r*rÓrÅrÔÚ_pirÙÚ_acos)r@rÌÚkappar*r³r~rÎrÐÚdrÏÚqÚfÚu3Úthetas rBr2zRandom.vonmisesvariateksù€ð —‘ˆØ �DŠ=Ü™6›8Ñ#Ð #à �%‰KˆØ ”�c˜A ™E‘kÓ"Ñ "ˆàÙ“ˆBÜ”S˜2‘X“ˆAà�Q˜‘U‘ ˆAÙ“ˆBØ�C˜!˜a™%‘KÒ 2¨#°©'´T¸!³WÑ)<Ò#<Øðð �!‰GˆØ �‰U�s˜Q ™U‘{Ñ #ˆÙ ‹XˆØ �Š8Øœ% ›(‘]¤eÑ+ˆEðˆ ðœ% ›(‘]¤eÑ+ˆEàˆ rDcóš—|dks|dkr td«‚|j}|dkDrštd|zdz «}|tz }||z} |«}d|cxkrdksnŒd|«z }t |d|z z «|z } |t | «z} ||z|z} ||| zz| z } | t zd| zz dk\s| t | «k\r| |zSŒz|dk(rt d|«z « |zS |«} t|ztz }|| z}|dkr |d|z z} nt ||z |z « } |«}|dkDr|| |dz zkr | |zS|t | «kr | |zSŒo)aîGamma 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 The mean (expected value) and variance of the random variable are: E[X] = alpha * beta Var[X] = alpha * beta ** 2 r»z*gammavariate: alpha and beta must be > 0.0r7r5gH¯¼šò×z>gËPÊÿÿï?r8)rfr*rÅÚLOG4r¬rÙÚ SG_MAGICCONSTÚ_e)r@ÚalphaÚbetar*ÚainvÚbbbÚcccrÎrÏÚvrArÐr~rÉr˜Úps rBr!zRandom.gammavariate•s¦€ð( �CŠ<˜4 3š;ÜÐIÓJÐ Jà—‘ˆØ �3Š;ô ˜˜u™ sÑ*Ó+ˆDØœ$‘,ˆCؘ$‘,ˆCàÙ“X�ؘbÔ, 9Ô,ØØ™6›8‘^�ܘ˜s R™x™Ó)¨DÑ0�ØœD ›G‘O�ؘ‘G˜b‘L�ؘ# ™'‘M AÑ%�Ø”}Ñ$ s¨Q¡wÑ.°#Ò5¸¼dÀ1»gºØ˜t™8�Oðð�cŠ\䘙v›x™Ó(Ð(¨4Ñ/Ð /ð Ù“H�ܘ%‘Z¤2Ñ%�ؘ‘E�ؘ’8ؘc E™kÑ*‘Aä˜q 1™u¨™oÓ.Ð.�AÙ“X�Ø�s’7ؘQ 5¨3¡;Ñ/Ò/Øð�t‘8ˆOðœ4  ›8’^ØØ�t‘8ˆOðrDcó\—|j|d«}|r|||j|d«zz Sy)aQBeta distribution. Conditions on the parameters are alpha > 0 and beta > 0. Returned values range between 0 and 1. The mean (expected value) and variance of the random variable are: E[X] = alpha / (alpha + beta) Var[X] = alpha * beta / ((alpha + beta)**2 * (alpha + beta + 1)) r7r»)r!)r@rérêÚys rBrzRandom.betavariateÚs9€ð6 × Ñ ˜e SÓ )ˆÙ ؘ˜D×-Ñ-¨d°CÓ8Ñ8Ñ9Ð 9ØrDcó8—d|j«z }|d|z zS)z3Pareto distribution. alpha is the shape parameter.r7gð¿rÁ)r@rérÉs rBr'zRandom.paretovariateús#€ð �$—+‘+“-Ñ ˆØ�T˜E‘\Ñ"Ð"rDcóR—d|j«z }|t|« d|z zzS)zfWeibull distribution. alpha is the scale parameter and beta is the shape parameter. r7)r*r¬)r@rérêrÉs rBr3zRandom.weibullvariates.€ð �$—+‘+“-Ñ ˆØœ˜a›˜ c¨D¡jÑ1Ñ1Ð1rDcóÜ—|dkr td«‚|dks|dk\r|dk(ry|dk(r|Std«‚|j}|dk(rt|«|k«S|dkDr||j|d|z «z S||zdkrFdx}}t d|z «}|s|S |t t |««|z «dzz }||kDr|S|dz }Œ/d }t ||zd|z z«}d d |zz} d d| zzd|zz} ||zdz}dd| z z } |«} | dz} dt| «z } t d| z| z | z| z|z«}|dks||kDrŒ@|«}| dk\r|| kr|S|sOdd| z z|z}t|d|z z «}t |dz|z«}t|dz«t||z dz«z}d }|| | | zz | zz z}t|«t|dz«z t||z dz«z |z zzkr|SŒï)ašBinomial random variable. Gives the number of successes for *n* independent trials with the probability of success in each trial being *p*: sum(random() < p for i in range(n)) Returns an integer in the range: 0 <= X <= n The mean (expected value) and variance of the random variable are: E[X] = n * p Var[x] = n * p * (1 - p) rzn must be non-negativer»r7z&p must be in the range 0.0 <= p <= 1.0r;rÃg$@TFgffffffò?g= ×£p=@gEØðôJY¶¿gaÃÓ+e™?ç{®Gáz„?gq= ×£pí?gÍÌÌÌÌÌ@r5gìQ¸…ë±?g¤p= ×£@gffffff@) rfr*r�rÚ_log2r�rÅÚ_fabsr¬Ú_lgamma)r@r|rïr*rArñr\Úsetup_completeÚspqr˜rZÚvrrÉÚusr}rîréÚlpqÚmÚhs rBrzRandom.binomialvariates{€ð" ˆqŠ5ÜÐ5Ó6Ð 6Ø �Š8�q˜C’xØ�CŠxØØ�CŠxØ�ÜÐEÓFÐ Fà—‘ˆð �Š6Ü™&›( Q™,Ó'Ð 'ð ˆsŠ7Ø�t×+Ñ+¨A¨s°Q©wÓ7Ñ7Ð 7à ˆq‰5�4Š<ðˆIˆA�Ü�c˜A‘g“ˆAÙØ�ØØ”VœE¡&£(›O¨aÑ/Ó0°1Ñ4Ñ4�Ø�q’5Ø�HØ�Q‘�ð ðˆä�A˜‘E˜S 1™WÑ%Ó&ˆØ �4˜#‘:Ñ ˆØ �f˜q‘jÑ  4¨!¡8Ñ +ˆØ �‰E�C‰KˆØ �C˜!‘G‰^ˆàᓈAØ �‰HˆAØ”u˜Q“x‘ˆBܘ˜a™ "™  qÑ(¨AÑ-°Ñ1Ó2ˆAØ�1Šu˜˜AšØñ“ˆAØ�TŠz˜a 2šgØ�ñ "Ø  a¡™¨3Ñ.�ܘ1  a¡™=Ó)�ܘA ™E Q™;Ó'�ܘA ™E“N¤W¨Q°©U°Q©YÓ%7Ñ7�Ø!%�Ø �˜!˜r B™w™-¨!Ñ+Ñ,Ñ ,ˆAÜ�A‹w˜!œg a¨!¡e›nÑ,¬w°q¸1±u¸q±yÓ/AÑAÀQÈÁUÈcÁMÑQÒQØ�ð5rDrj)Nr:)r»r7N©r»r7)r7)r;rÃ)&Ú__name__Ú __module__Ú __qualname__Ú__doc__r_rCr-r$r.rlrnrpryruÚBPFrvrrr(rŽr+r)rr/r,rr1r0r&r"r%r r2r!rr'r3rÚ __classcell__)r]s@rBrrnsÒø„ñ ð€Góõ$ôLAô6òBòò3ò ò(ð9:¸3¹ó'ð&-€Jò=ð%)¨tó'3òR&ò.ò$ð/3ô]ð~#+¸tÀqô#+òP +ó1ó2ó*$òL3ó2ò((òTCòJò@#ò 2÷VrDrcó6—eZdZdZd„Zd„Zd„Zd„Zd„ZexZ Z y)rzÞAlternate random number generator using sources provided by the operating system (such as /dev/urandom on Unix or CryptGenRandom on Windows). Not available on all systems (see os.urandom() for details). cóR—tjtd««dz tzS)z7Get the next random number in the range 0.0 <= X < 1.0.rFr=)rRrSÚ_urandomÚ RECIP_BPFrks rBr*zSystemRandom.randomus€ä—‘œx¨›{Ó+¨qÑ0´IÑ=Ð=rDcó„—|dkr td«‚|dzdz}tjt|««}||dz|z z S)z:getrandbits(k) -> x. Generates an int with k random bits.rz#number of bits must be non-negativerFr‡)rfrRrSr )r@r}ÚnumbytesrAs rBr#zSystemRandom.getrandbitsysI€à ˆqŠ5ÜÐBÓCÐ Cؘ‘E˜a‘<ˆÜ �N‰Nœ8 HÓ-Ó .ˆØ�X ‘\ AÑ%Ñ&Ð&rDcó—t|«S)r†)r rŠs rBr(zSystemRandom.randbytes�s€ô˜‹{ÐrDcó—y)zò'òò òPð*Ð)€H‰xrDrcó6—ddlm}m}ddlm}|«}t d|«D�cgc]}||Ž‘Œ }}|«} ||«} ||| «} t |«} t|«} t| |z d›d|›d|j›|›�«td| | | | fz«ycc}w)Nr)ÚstdevÚfmean)Ú perf_counterz.3fz sec, z times z"avg %g, stddev %g, min %g, max %g ) Ú statisticsrrÚtimerr¼ÚminÚmaxÚprintr)r|ÚfuncrrÚmeanrÚt0r ÚdataÚt1ÚxbarrÍrÆrÇs rBÚ_test_generatorr$¶s¡€ß/Ý!á ‹€BÜ!(¨¨qÔ!1Ó 2Ñ!1˜A‰D�$ŠKÐ!1€DÐ 2Ù ‹€Bá �‹:€DÙ �$˜Ó €EÜ ˆd‹)€CÜ ˆt‹9€Dä ˆR�"‰W�SˆM˜ ˜s '¨$¯-©-¨¸¸Ð AÔBÜ Ð /°4¸ÀÀTÐ2JÑ JÕKùò 3s¤ Bcóh—t|td«t|td«t|td«t|td«t|t d«t|t d«t|t d«t|t d«t|t d«t|t d«t|t d «t|t d «t|t d «t|t d «t|t d «t|td«t|td«t|td«y)Nrbr)ég333333ã?)édgè?)rõr7)çš™™™™™¹?r7)r(r5)rÃr7)gÍÌÌÌÌÌì?r7)r7r7)r5r7)g4@r7)gi@r7)ç@r))r»r7gUUUUUUÕ?) r$r*r&r%r2rr!r"rr0)ÚNs rBÚ_testr+ÇsÛ€Ü�A”v˜rÔ"Ü�A”} jÔ1Ü�A”~ zÔ2Ü�A”¨ Ô3Ü�A”¨ Ô3Ü�A”¨ Ô4Ü�A”| [Ô1Ü�A”| ZÔ0Ü�A”| ZÔ0Ü�A”| ZÔ0Ü�A”| ZÔ0Ü�A”| ZÔ0Ü�A”| ZÔ0Ü�A”| [Ô1Ü�A”| \Ô2Ü�A”u˜jÔ)Ü�A”{ JÔ/Ü�A”zÐ#8Õ9rDÚfork)Úafter_in_childÚ__main__)i')arÚwarningsrr€Úmathrr¬rrÙrrÝrrèrr«r rÅr rÞr rÔr rÕr rÓrr�rr½rrørr÷rröÚosrr Ú_collections_abcrr¦Úoperatorrr�Ú itertoolsrr¨rr¼rrªÚ_osÚ_randomÚ_sha2rrTÚ ImportErrorÚhashlibÚ__all__rËrærçrr rŽrrÚ_instr-r*r1r0r)rr+r,r/rr&r%r r2r!r"rrr'r3r$r.r#r(r$r+ÚhasattrÚregister_at_forkrrbrDrBÚr>sýðñ.õh#ßLÕLßGÓGßEÑEß@Ñ@Ý"Ý2Ý$ßBÝ$ÛÛð*å'ò  €ð:‘D˜“J‘¡ s£Ñ+€ Ù ˆCƒy€Ø‘d˜3“i‘€ Ø€Ø �#�‰I€ Ø€ôw ˆW�^‰^ôw ô|"*�6ô"*ñX ‹€Ø ‡z�z€Ø �‰€Ø �-‰-€Ø × Ñ € Ø �-‰-€Ø �‰€Ø �O‰O€ Ø �‰€Ø �-‰-€Ø �-‰-€Ø×#Ñ#€ Ø×%Ñ%€Ø×Ñ€ Ø×'Ñ'€Ø×!Ñ!€ Ø � ‰ €Ø×Ñ€ Ø×'Ñ'€Ø×#Ñ#€ Ø×%Ñ%€Ø �>‰>€Ø �>‰>€Ø×Ñ€ Ø �O‰O€ ò Ló":ñ0 ˆ3�ÔØ€C×Ѩ¯ © Õ3ð ˆzÒÙ …Gðøð}ò*ç)Ð)ð*úsÁ HÈ H,È+H,