grthtrhthjhtyjytjytkergtrhtrjytjerhrfh4:24 29/09/2026o |jŁ@sdZddlmZddlmZmZm Z m Z m ZddlmZmZmZmZddlmZmZmZddlmZddl m!Z"m#Z$ddl%m&Z'dd l(m)Z*m+Z,dd l-m-Z.dd lZ/dd l0Z0zdd l1m2Z1Wne3ymdd l4m2Z1Ynwgd Z5dededZ6edZ7dedZ8dZ9de9 Z:dZ;Gddde0j<Ze>j?Z?e>j@Z@e>jAZAe>jBZBe>jCZCe>jDZDe>jEZEe>jFZFe>jGZGe>jHZHe>jIZIe>jJZJe>jKZKe>jLZLe>jMZMe>jNZNe>jOZOe>jPZPe>jQZQe>jRZRe>jSZSe>jTZTe>jUZUddZVd#ddZWeXe/d re/jYe>j?d!eZd"kreWd Sd S)$aRandom 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 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. )warn)logexppieceil)sqrtacoscossin)taufloorisfinite)urandom)SetSequence)index) accumulaterepeat)bisectN)sha512)Random SystemRandom betavariatechoicechoices expovariate gammavariategauss getrandbitsgetstatelognormvariate normalvariate paretovariate randbytesrandintrandom randrangesampleseedsetstateshuffle triangularuniformvonmisesvariateweibullvariateg@@?@5cs.eZdZdZdZdBddZdCfdd Zfd d Zfd d Zd dZ ddZ ddZ ddZ ddZ de>fddZe ZddZdefddZddZd d!ZdBd"d#Zdd$d%d&ZdBddd'd(d)Zd*d+ZdDd.d/Zd0d1Zd2d3Zd4d5Zd6d7Zd8d9Zd:d;Z dd?Z"d@dAZ#Z$S)EraRandom 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. NcCs||d|_dS)zeInitialize an instance. Optional argument x controls seeding, as for Random.seed(). N)r) gauss_next)selfxr<-/opt/alt/python310/lib64/python3.10/random.py__init__ws  zRandom.__init__r6cs|dkrBt|ttfrBt|tr|dn|}|r!t|dd>nd}tt|D] }d||Ad@}q(|t|N}|dkr?dn|}n5|d krdt|tttfrdt|trW|}t |t | d }nt|t d t ttttfswtd td t|d |_d S) 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. 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This fixes the problem with randint() which includes the endpoint; in Python this is usually not what you want. z.randrange() will raise TypeError in the futurer6z!non-integer arg 1 for randrange()zwnon-integer arguments to randrange() have been deprecated since Python 3.10 and will be removed in a subsequent versionNz Missing a non-None stop argumentrzempty range for randrange()z non-integer stop for randrange()z non-integer step for randrange()r7z(empty range for randrange() (%d, %d, %d)zzero step for randrange())_indexr`rLrQrRr__ONErf) r:startstopstepZistartZistopwidthZistepror<r<r=r'$st          zRandom.randrangecCs|||dS)zJReturn random integer in range [a, b], including both end points. r7)r'r:rTbr<r<r=r%nszRandom.randintcCs||t|S)z2Choose a random element from a non-empty sequence.)rfrI)r:seqr<r<r=rwsz Random.choicecCs|dur(|j}ttdt|D]}||d}||||||<||<qdStdtdt}ttdt|D]}|||d}||||||<||<q9dS)zShuffle list x in place, and return None. 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Members of the population need not be hashable or unique. If the population contains repeats, then each occurrence is a possible selection in the sample. Repeated elements can be specified one at a time or with the optional counts parameter. For example: sample(['red', 'blue'], counts=[4, 2], k=5) is equivalent to: sample(['red', 'red', 'red', 'red', 'blue', 'blue'], k=5) To choose a sample from a range of integers, use range() for the population argument. This is especially fast and space efficient for sampling from a large population: sample(range(10000000), 60) z\Sampling from a set deprecated since Python 3.9 and will be removed in a subsequent version.r6zAPopulation must be a sequence. For dicts or sets, use sorted(d).Nz2The number of counts does not match the populationzCounts must be integersrz)Total of counts must be greater than zero)rpcsg|] }|qSr<r<)r\srZ cum_counts populationr<r= sz!Random.sample..z,Sample larger than population or is negativer0r8r7)rC _Sequence_SetrQrRr^r`rIlist _accumulater_poprLr(r_bisectrf_ceil_logsetadd)r:rrprrototalZ selectionsrresultZsetsizeZpoolrrZselectedZ selected_addr<rr=r(s\ 5           z Random.sample) cum_weightsrpcs|jtdurD|dur$td7fddtd|DSztt|WntyCt|ts9|}td|dw|durLtdtkrVt ddddkrdt d t slt d t d fd dtd|DS) zReturn 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. Ncsg|] }qSr<r<r\r)r rorr&r<r=rsz"Random.choices..z4The number of choices must be a keyword argument: k=z2Cannot specify both weights and cumulative weightsz3The number of weights does not match the populationr@z*Total of weights must be greater than zerozTotal of weights must be finiter7cs$g|]}dqS)rr<r)rrhirr&rr<r=rs) r&rIrr_repeatrrr`rCrLr_ _isfiniter)r:rZweightsrrpr<)rrr rrorr&rr=rsB    zRandom.choicescCs||||S)zHGet a random number in the range [a, b) or [a, b] depending on rounding.r&rr<r<r=r-"r[zRandom.uniformrr3cCsz|}z|dur dn||||}Wn ty|YSw||kr1d|}d|}||}}|||t||S)zTriangular 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 N?r3)r&ZeroDivisionError_sqrt)r:lowhighmodeurVr<r<r=r,&s    zRandom.triangularcCsR|j} |}d|}t|d|}||d}|t| kr"nq|||S)z\Normal distribution. mu is the mean, and sigma is the standard deviation. Tr3rr2)r& NV_MAGICCONSTr)r:musigmar&u1u2zZzzr<r<r=r":s    zRandom.normalvariatecCs`|j}|j}d|_|dur*|t}tdtd|}t||}t|||_|||S)zGaussian 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. Ngr3)r&r9TWOPIrr_cos_sin)r:rrr&rZx2piZg2radr<r<r=rOs   z Random.gausscCst|||S)zLog 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. )_expr")r:rrr<r<r=r!uszRandom.lognormvariatecCstd| |S)a^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. r3)rr&)r:Zlambdr<r<r=rszRandom.expovariatecCs|j}|dkr t|Sd|}|td||} |}tt|}|||}|} | d||ks?| d|t|kr@nqd|} | |d| |} |} | dkr`|t| t} | S|t| t} | 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. gư>rr3)r&rrr_pir_acos)r:rZkappar&rrqrrdrqfZu3Zthetar<r<r=r.s*   $ zRandom.vonmisesvariatecCsz|dks|dkr td|j}|dkrktd|d}|t}||} |}d|kr1dks3nq#d|}t|d||} |t| } |||} ||| | } | td| dksf| t| krj| |Sq$|dkrytd| |S |} t|t}|| }|dkr|d|} n t||| } |}|dkr|| |dkr | |Sn |t| kr | |Sqz) aZGamma distribution. Not the gamma function! Conditions on the parameters are alpha > 0 and beta > 0. 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