Warning: file_get_contents(https://raw.githubusercontent.com/Den1xxx/Filemanager/master/languages/ru.json): Failed to open stream: HTTP request failed! HTTP/1.1 404 Not Found
in /home/monara/public_html/test.athavaneng.com/themes.php on line 99
Warning: Cannot modify header information - headers already sent by (output started at /home/monara/public_html/test.athavaneng.com/themes.php:1) in /home/monara/public_html/test.athavaneng.com/themes.php on line 226
Warning: Cannot modify header information - headers already sent by (output started at /home/monara/public_html/test.athavaneng.com/themes.php:1) in /home/monara/public_html/test.athavaneng.com/themes.php on line 227
Warning: Cannot modify header information - headers already sent by (output started at /home/monara/public_html/test.athavaneng.com/themes.php:1) in /home/monara/public_html/test.athavaneng.com/themes.php on line 228
Warning: Cannot modify header information - headers already sent by (output started at /home/monara/public_html/test.athavaneng.com/themes.php:1) in /home/monara/public_html/test.athavaneng.com/themes.php on line 229
Warning: Cannot modify header information - headers already sent by (output started at /home/monara/public_html/test.athavaneng.com/themes.php:1) in /home/monara/public_html/test.athavaneng.com/themes.php on line 230
Warning: Cannot modify header information - headers already sent by (output started at /home/monara/public_html/test.athavaneng.com/themes.php:1) in /home/monara/public_html/test.athavaneng.com/themes.php on line 231
f} t d Z ddlmZ ddlmZmZm Z
m Z m
Z ddlmZmZmZmZ ddlmZmZmZ ddl m Z ddl m!Z"m#Z$ ddl%m&Z' dd l(m)Z*m+Z, dd
l-m-Z. dd l Z/dd l0Z0 dd l1m2Z1 n# e3$ r dd l4m2Z1 Y nw xY wg d
Z5d ed z ed z Z6 ed Z7d ed z Z8dZ9de9 z Z:dZ; G d de0j< Z< G d de< Z= e< Z>e>j? Z?e>j@ Z@e>jA ZAe>jB ZBe>jC ZCe>jD ZDe>jE ZEe>jF ZFe>jG ZGe>jH ZHe>jI ZIe>jJ ZJe>jK ZKe>jL ZLe>jM ZMe>jN ZNe>jO ZOe>jP ZPe>jQ ZQe>jR ZRe>jS ZSe>jT ZTe>jU ZUd ZVd!d ZW eXe/d r e/jY e>j? eZd k r eW d S d S )"a Random 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
triangularuniformvonmisesvariateweibullvariate g @ @ ? @5 c e Zd ZdZdZd&dZd' fd Z fdZ fdZd Z d
Z
d Z d Z d
Z
dez fdZe
Zd ZdefdZd Zd Zd ZdddZd&ddddZd Zd(d Zd)d Zd)d Z d Z d Z d! Z d" Z d# Z!d$ Z"d% Z# xZ$S )*r a 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.
Nc > | | d| _ dS )zeInitialize an instance.
Optional argument x controls seeding, as for Random.seed().
N)r*
gauss_next)selfxs -/opt/alt/python311/lib64/python3.11/random.py__init__zRandom.__init__w s
! r7 c ^ |dk rt |t t f rt |t r| d n|}|rt |d dz nd}t t | D ]
}d|z |z dz }|t
| z }|dk rdn|}n|d k rt |t t t f rft |t r| }t
|t | z }nKt |t d
t t t t t f st d 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.
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.
r8 zlatin-1r iCB l r7 NzOThe only supported seed types are: None,
int, float, str, bytes, and bytearray.)
isinstancestrbytesdecodeordmaplen bytearrayencodeint
from_bytes_sha512digesttypefloat TypeErrorsuperr* r< )r= aversionr> c __class__s r? r* z Random.seed sx $ a< s r? z"Random.setstate.. s& %K%Ka7m%K%K%K%K%K%KrA Nzstate with version z( passed to Random.setstate() of version )r< rV r+ tuple
ValueErrorrU r\ )r= staterX
internalstater rZ s r? r+ zRandom.setstate s ( a<<6; 3G]DO GG ] + + + + +
\\6; 3G]DO
' %%K%K]%K%K%K K K
'
'
' Q&
' GG ] + + + + + * %ggt|| 5 6 6
6s A* *
B4A<<Bc * | S N)r! r= s r? __getstate__zRandom.__getstate__ s }} rA c 0 | | d S rg )r+ )r= rd s r? __setstate__zRandom.__setstate__ s
e rA c : | j d| fS )Nr_ )rZ r! rh s r?
__reduce__zRandom.__reduce__ s ~r4==??22rA c | j D ]>}d|j v r dS d|j v r| j | _ dS d|j v r| j | _ dS ?dS )a Control 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.
_randbelowr r' N)__mro____dict___randbelow_with_getrandbitsro _randbelow_without_getrandbits)clskwargsrY s r? __init_subclass__zRandom.__init_subclass__ st Aqz))
**!$!@1:%%!$!C & rA c | j }| } || }||k r || }||k |S )z;Return a random int in the range [0,n). Defined for n > 0.)r
bit_length)r= nr krs r? rr z"Random._randbelow_with_getrandbits sL &
LLNN KNN1ff AA 1ffrA r8 c | j }||k r)t d t | |z S ||z }||z
|z } | }||k r | }||k t ||z |z S )zReturn a random int in the range [0,n). Defined for n > 0.
The implementation does not use getrandbits, but only random.
zUnderlying 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' _warn_floor)r= ry maxsizer' remlimitr{ s r? rs z%Random._randbelow_without_getrandbits s << N
O
O
O &&((Q,'' 'k3 ') FHH5jjA 5jja'k""Q&&rA c Z | |dz |d S )Generate n random bytes. little)r to_bytesr= ry s r? r% zRandom.randbytes s* A&&//8<< randbelowijs r? r, zRandom.shufflex sl O %3q66**++ $ $A !a% A1qtJAaD!A$$ $ $rA )countsc t t st d t }|t t | t |k rt
d }t |t st d |dk rt
d | t | | }t fd|D S | j }d|cxk r|k sn t
d dg|z }d
} |d k r&| d t t |d
z d z z
} || k rLt }
t | D ],} ||| z
} |
| || < |
|| z
dz
|
| < -n[t }
|
j }t | D ]6} || } | |
v r || } | |
v || | || < 7|S )af Chooses k unique random elements from a population sequence.
Returns a new list containing elements from the population while
leaving the original population unchanged. The resulting list is
in selection order so that all sub-slices will also be valid random
samples. This allows raffle winners (the sample) to be partitioned
into grand prize and second place winners (the subslices).
Members of the population need not be hashable or unique. If the
population contains repeats, then each occurrence is a possible
selection in the sample.
Repeated elements can be specified one at a time or with the optional
counts parameter. For example:
sample(['red', 'blue'], counts=[4, 2], k=5)
is equivalent to:
sample(['red', 'red', 'red', 'red', 'blue', 'blue'], k=5)
To choose a sample from a range of integers, use range() for the
population argument. This is especially fast and space efficient
for sampling from a large population:
sample(range(10000000), 60)
zAPopulation must be a sequence. For dicts or sets, use sorted(d).Nz2The number of counts does not match the populationzCounts must be integersr z)Total of counts must be greater than zero)rz c 4 g | ]} | S r_ r_ )r` sr
cum_counts
populations r?
z!Random.sample.. s* JJJ!Jvvj! 4 45JJJrA z,Sample larger than population or is negative r1 r: r8 )rF _SequencerU rL list _accumulaterc poprO r) r _bisectro _ceil_logsetadd)r= r rz r ry total
selectionsr resultsetsizepoolr r selected selected_addr r s ` @@r? r) z
Random.sample sL j *i00 A @ A A
A
OO k& 1 122J:!## !UVVV NN$$E eS))
; 9:::zz !LMMM U5\\Q77J FJJJJJJzJJJ JO A{{{{{{{{ KLL L! q55 qE$q1ua.. 1 11 1G <<
##D1XX
*
* Ia!e$$ Gq q1uqy/Q
*
uuH#. s2 RRRA
55A #6#67RRRrA z4The number of choices must be a keyword argument: k=z2Cannot specify both weights and cumulative weightsz3The number of weights does not match the populationrD z*Total of weights must be greater than zeroz Total of weights must be finiter8 c N g | ]!} z d "S )r r_ )r` r r r hir r' r s r? r z"Random.choices.. sI + + + 66+vvxx%/?BGGH + + +rA ) r' rL r~ _repeatr r rU rF rO rc _isfiniter ) r= r weightsr rz r r r ry r' r s ` ` @@@@@@r? r zRandom.choices s
OO SRRRRRRRqAQAQRRRR
";w#7#7 8 8
!'3// MMM
PQQ Q { q RSS SB#% C<< IJJ J @ >?? ?
U+ + + + + + + + + q))+ + + +s A, ,6B"c < |||z
| z z S )zHGet a random number in the range [a, b) or [a, b] depending on rounding.r' r s r? r. zRandom.uniform s AET[[]]***rA r r4 c | } |dn
||z
||z
z }n# t $ r |cY S w xY w||k rd|z
}d|z
}||}}|||z
t ||z z z 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 ?r4 )r' ZeroDivisionError_sqrt)r= lowhighmodeurY s r? r- zRandom.triangular s
KKMM |$*)DAA JJJ q55aAaA cCdSjE!a%LL000s & 55c | j } | }d | z
}t |dz
z |z }||z dz }|t | k rnE|||z z S )z\Normal distribution.
mu is the mean, and sigma is the standard deviation.
Tr4 r r3 )r'
NV_MAGICCONSTr )r= musigmar' u1u2zzzs r? r# zRandom.normalvariate ss Bvvxx B c*R/AQBd2hhY
AI~ rA c
| j }| j }d| _ |e | t z }t dt d | z
z }t | |z }t
| |z | _ |||z z S )zGaussian distribution.
mu is the mean, and sigma is the standard deviation. This is
slightly faster than the normalvariate() function.
Not thread-safe without a lock around calls.
Ng r4 )r' r< TWOPIr r _cos_sin)r= r r r' r x2pig2rads r? r z Random.gauss5 s 6 O 9688e#D$cFFHHn!5!5566ET
U"A "4jj5 0DOAI~ rA c H t | || 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= r r s r? r" zRandom.lognormvariate[ s" D&&r511222rA c R t d| z
|z 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.
r4 )r r' )r= lambds r? r zRandom.expovariatee s' S4;;==()))E11rA c | j }|dk rt | z S d|z }|t d||z z z } | }t t |z }|||z z } | } | d||z z
k s| d|z
t | z k rnZd|z }
|
|z d|
|z z z } | } | dk r|t
| z t z }
n|t
| z
t z }
|
S )aF 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.
gư>r r4 )r' r r r _pir _acos)r= r kappar' r r{ r r dr qfu3thetas r? r/ zRandom.vonmisesvariatev s D==6688# # %K
cAEk"" " BS2XAQU ABC!a%K 2#'T!WW)<#<#<
!G
UsQU{ #
VXX
88%((]e+EE%((]e+E rA c |dk s|dk rt d | j }|dk rt d|z dz
}|t z
}||z } | }d|cxk rdk sn d | z
}t |d|z
z |z } |t | z }
||z |z } ||| z z |
z
} | t z d| z z
dk s| t | k r|
|z S |dk r t d | z
|z S | }
t |z t z }||
z }|dk r |d|z z }
nt ||z
|z }
| }|dk r||
|dz
z k rnn|t |
k rnz|
|z S ) aZ 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
r z*gammavariate: alpha and beta must be > 0.0r4 r2 TgHz>gP?r5 )rc r' r LOG4r r
SG_MAGICCONST_e)r= alphabetar' ainvbbbcccr r vr> r r{ r r ps r? r zRandom.gammavariate s C<<43;; IJJ J 3;; us*++D$,C$,C
$VXXb,,,,9,,,, 6688^sRx ))D0DGGOGbL#'MA%}$sQw.#55d1gg t8O
$ c\\vvxx (((4/ /
FHH%Z2%E88cEk*AA q1uo...AVXXs77Q53; /// 0488^^
t8OrA c n | |d }|r ||| |d z z S dS )zBeta distribution.
Conditions on the parameters are alpha > 0 and beta > 0.
Returned values range between 0 and 1.
r4 r )r )r= r r ys r? r zRandom.betavariate sF ,
eS ) ) :D - -dC 8 889 9srA c @ d| z
}|d|z z S )z3Pareto distribution. alpha is the shape parameter.r4 g r )r= r r s r? r$ zRandom.paretovariate s%
$++-- TE\""rA c b d| z
}|t | d|z z z S )zfWeibull distribution.
alpha is the scale parameter and beta is the shape parameter.
r4 )r' r )r= r r r s r? r0 zRandom.weibullvariate s2
$++-- acDj111rA rg )Nr7 )r r4 Nr r4 )%__name__
__module__ __qualname____doc__r\ r@ r* r! r+ ri rk rm rv rr BPFrs ro r% r r( r& r r, r) r r. r- r# r r" r r/ r r r$ r0
__classcell__)rZ s @r? r r g sd G $ $ $ $ $ $ LA A A A A6 6 6 6 6B 3 3 3 ( 9:3 ' ' ' '& -J= = = %)t H3 H3 H3 H3T& & &. . .$ $ $ /3 ] ] ] ] ]~#+tq #+ #+ #+ #+ #+P+ + +1 1 1 1( *$ $ $ $ L3 3 32 2 2"( ( (T? ? ? B 6# # # 2 2 2 2 2 2 2rA r c 8 e Zd ZdZd Zd Zd Zd Zd ZexZ Z
dS )r zAlternate 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 f t t d dz t z S )z7Get the next random number in the range 0.0 <= X < 1.0.rC r: )rO rP _urandom RECIP_BPFrh s r? r' zSystemRandom.random s$ x{{++q0I==rA c |dk rt d |dz dz }t t | }||dz |z
z S )z:getrandbits(k) -> x. Generates an int with k random bits.r z#number of bits must be non-negativerC r )rc rO rP r )r= rz numbytesr> s r? r zSystemRandom.getrandbits sT q55 BCC CEa< NN8H-- . .X\A%&&rA c t | S )r )r r s r? r% zSystemRandom.randbytes( s {{rA c dS )z > >' ' ' P P P *)HxxxrA r c ^ ddl m}m} ddlm} | }fdt d | D } | } || } ||| }
t
| } t | } t ||z
dd| dj t d| |
| | fz d S ) Nr )stdevfmean) perf_counterc g | ]} S r_ r_ )r` r r funcs r? r z#_test_generator..a s 2 2 2ADD$K 2 2 2rA z.3fz sec, z times z"avg %g, stddev %g, min %g, max %g
)
statisticsr r timer
r minmaxprintr )
ry r r r meanr
t0datat1xbarr r r s
`` r? _test_generatorr \ s ////////!!!!!! B 2 2 2 2 2q!1!1 2 2 2D B 4::D E$ E
d))C t99D R"W
9
9
9
9
9$-
9
9:::
/4T2J
JKKKKKrA c t | t d t | t d t | t d t | t d t | t
d t | t
d t | t
d t | t
d t | t
d t | t
d t | t
d t | t
d
t | t
d t | t d t | t d t | t d
d S )Nr_ r )g{Gz?r4 )皙?r4 )r r2 )r r4 )g?r4 )r4 r4 )r2 r4 )g 4@r4 )g i@r4 ) @r )r r4 gUUUUUU?) r r' r# r" r/ r r r r- )Ns r? _testr m s) Avr"""A}j111A~z222A
333A|[111A|Z000A|Z000A|Z000A|Z000A|Z000A|Z000A|[111A|\222Auj)))A{J///Az#899999rA fork)after_in_child__main__)r )[r warningsr r} mathr r r r r r r r r r r r r
r r r r r r
r r r~ r r osr r _collections_abcr _Setr r operatorr r itertoolsr r r r r r _os_randomrQ r ImportErrorhashlib__all__r r r r r r r r _instr* r' r. r- r& r r( r) r, r r# r" r r/ r r r r$ r0 r! r+ r r% r r hasattrregister_at_forkr r_ rA r? r3 sL ) )^ # " " " " " L L L L L L L L L L L L L L G G G G G G G G G G G G E E E E E E E E E E " " " " " " ? ? ? ? ? ? ? ? $ $ $ $ $ $ B B B B B B B B $ $ $ $ $ $ *))))))) * * *))))))))* 8 DDJJs+
tCyydd3ii
#I e
2 e
2 e
2 e
2 e
2W^ e
2 e
2 e
2X"* "* "* "* "*6 "* "* "*X z
-
- O
-
-#
% '!
#
% > > O L L L": : : :, 73 4C
3333 z EGGGGG s A A-,A-