FM / python3.9

/usr/lib64/python3.9

Name	Size	Mode	Actions
asyncio/	-	0755	rm
collections/	-	0755	rm
concurrent/	-	0755	rm
config-3.9-x86_64-linux-gnu/	-	0755	rm
ctypes/	-	0755	rm
curses/	-	0755	rm
dbm/	-	0755	rm
distutils/	-	0755	rm
email/	-	0755	rm
encodings/	-	0755	rm
ensurepip/	-	0755	rm
html/	-	0755	rm
http/	-	0755	rm
importlib/	-	0755	rm
json/	-	0755	rm
lib-dynload/	-	0755	rm
lib2to3/	-	0755	rm
logging/	-	0755	rm
multiprocessing/	-	0755	rm
pydoc_data/	-	0755	rm
site-packages/	-	0755	rm
sqlite3/	-	0755	rm
unittest/	-	0755	rm
urllib/	-	0755	rm
venv/	-	0755	rm
wsgiref/	-	0755	rm
xml/	-	0755	rm
xmlrpc/	-	0755	rm
zoneinfo/	-	0755	rm
__pycache__/	-	0755	rm
abc.py	4920	0644	edit dl rm
aifc.py	32605	0644	edit dl rm
antigravity.py	500	0644	edit dl rm
argparse.py	98119	0644	edit dl rm
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colorsys.py	4064	0644	edit dl rm
compileall.py	20105	0644	edit dl rm
configparser.py	54584	0644	edit dl rm
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cProfile.py	6345	0755	edit dl rm
crypt.py	3819	0644	edit dl rm
csv.py	16144	0644	edit dl rm
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decimal.py	320	0644	edit dl rm
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doctest.py	104568	0644	edit dl rm
enum.py	39440	0644	edit dl rm
filecmp.py	10024	0644	edit dl rm
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fnmatch.py	6004	0644	edit dl rm
formatter.py	15143	0644	edit dl rm
fractions.py	24323	0644	edit dl rm
ftplib.py	35496	0644	edit dl rm
functools.py	38881	0644	edit dl rm
genericpath.py	5246	0644	edit dl rm
getopt.py	7489	0644	edit dl rm
getpass.py	5990	0644	edit dl rm
gettext.py	27266	0644	edit dl rm
glob.py	5823	0644	edit dl rm
graphlib.py	9573	0644	edit dl rm
gzip.py	21772	0644	edit dl rm
hashlib.py	8069	0644	edit dl rm
heapq.py	22877	0644	edit dl rm
hmac.py	8043	0644	edit dl rm
imaplib.py	55256	0644	edit dl rm
imghdr.py	3808	0644	edit dl rm
imp.py	10536	0644	edit dl rm
inspect.py	118235	0644	edit dl rm
io.py	3541	0644	edit dl rm
ipaddress.py	80924	0644	edit dl rm
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LICENSE.txt	13937	0644	edit dl rm
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numbers.py	10338	0644	edit dl rm
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os.py	39065	0644	edit dl rm
pathlib.py	51134	0644	edit dl rm
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pickle.py	64920	0644	edit dl rm
pickletools.py	93486	0644	edit dl rm
pipes.py	8916	0644	edit dl rm
pkgutil.py	24276	0644	edit dl rm
platform.py	40601	0755	edit dl rm
plistlib.py	28248	0644	edit dl rm
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profile.py	22881	0755	edit dl rm
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pydoc.py	109599	0755	edit dl rm
py_compile.py	8203	0644	edit dl rm
queue.py	11496	0644	edit dl rm
quopri.py	7266	0755	edit dl rm
random.py	31484	0644	edit dl rm
re.py	15861	0644	edit dl rm
reprlib.py	5267	0644	edit dl rm
rlcompleter.py	7648	0644	edit dl rm
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sched.py	6442	0644	edit dl rm
secrets.py	2036	0644	edit dl rm
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socket.py	36915	0644	edit dl rm
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string.py	10566	0644	edit dl rm
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subprocess.py	83564	0644	edit dl rm
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sysconfig.py	25557	0644	edit dl rm
tabnanny.py	11406	0755	edit dl rm
tarfile.py	112939	0755	edit dl rm
telnetlib.py	23254	0644	edit dl rm
tempfile.py	27963	0644	edit dl rm
textwrap.py	19407	0644	edit dl rm
this.py	1003	0644	edit dl rm
threading.py	54176	0644	edit dl rm
timeit.py	13480	0755	edit dl rm
token.py	2368	0644	edit dl rm
tokenize.py	25883	0644	edit dl rm
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__future__.py	5147	0644	edit dl rm
__phello__.foo.py	64	0644	edit dl rm

Edit: /usr/lib64/python3.9/random.py (31484B)

"""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.

"""

# Translated by Guido van Rossum from C source provided by
# Adrian Baddeley.  Adapted by Raymond Hettinger for use with
# the Mersenne Twister  and os.urandom() core generators.

from warnings import warn as _warn
from math import log as _log, exp as _exp, pi as _pi, e as _e, ceil as _ceil
from math import sqrt as _sqrt, acos as _acos, cos as _cos, sin as _sin
from math import tau as TWOPI, floor as _floor
from os import urandom as _urandom
from _collections_abc import Set as _Set, Sequence as _Sequence
from itertools import accumulate as _accumulate, repeat as _repeat
from bisect import bisect as _bisect
import os as _os
import _random

try:
    # hashlib is pretty heavy to load, try lean internal module first
    from _sha512 import sha512 as _sha512
except ImportError:
    # fallback to official implementation
    from hashlib import sha512 as _sha512

__all__ = [
    "Random",
    "SystemRandom",
    "betavariate",
    "choice",
    "choices",
    "expovariate",
    "gammavariate",
    "gauss",
    "getrandbits",
    "getstate",
    "lognormvariate",
    "normalvariate",
    "paretovariate",
    "randbytes",
    "randint",
    "random",
    "randrange",
    "sample",
    "seed",
    "setstate",
    "shuffle",
    "triangular",
    "uniform",
    "vonmisesvariate",
    "weibullvariate",
]

NV_MAGICCONST = 4 * _exp(-0.5) / _sqrt(2.0)
LOG4 = _log(4.0)
SG_MAGICCONST = 1.0 + _log(4.5)
BPF = 53        # Number of bits in a float
RECIP_BPF = 2 ** -BPF

class Random(_random.Random):
    """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.

"""

VERSION = 3     # used by getstate/setstate

def __init__(self, x=None):
        """Initialize an instance.

Optional argument x controls seeding, as for Random.seed().
        """

self.seed(x)
        self.gauss_next = None

def seed(self, a=None, version=2):
        """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.

"""

if version == 1 and isinstance(a, (str, bytes)):
            a = a.decode('latin-1') if isinstance(a, bytes) else a
            x = ord(a[0]) << 7 if a else 0
            for c in map(ord, a):
                x = ((1000003 * x) ^ c) & 0xFFFFFFFFFFFFFFFF
            x ^= len(a)
            a = -2 if x == -1 else x

elif version == 2 and isinstance(a, (str, bytes, bytearray)):
            if isinstance(a, str):
                a = a.encode()
            a = int.from_bytes(a + _sha512(a).digest(), 'big')

elif not isinstance(a, (type(None), int, float, str, bytes, bytearray)):
            _warn('Seeding based on hashing is deprecated\n'
                  'since Python 3.9 and will be removed in a subsequent '
                  'version. The only \n'
                  'supported seed types are: None, '
                  'int, float, str, bytes, and bytearray.',
                  DeprecationWarning, 2)

super().seed(a)
        self.gauss_next = None

def getstate(self):
        """Return internal state; can be passed to setstate() later."""
        return self.VERSION, super().getstate(), self.gauss_next

def setstate(self, state):
        """Restore internal state from object returned by getstate()."""
        version = state[0]
        if version == 3:
            version, internalstate, self.gauss_next = state
            super().setstate(internalstate)
        elif version == 2:
            version, internalstate, self.gauss_next = state
            # In version 2, the state was saved as signed ints, which causes
            #   inconsistencies between 32/64-bit systems. The state is
            #   really unsigned 32-bit ints, so we convert negative ints from
            #   version 2 to positive longs for version 3.
            try:
                internalstate = tuple(x % (2 ** 32) for x in internalstate)
            except ValueError as e:
                raise TypeError from e
            super().setstate(internalstate)
        else:
            raise ValueError("state with version %s passed to "
                             "Random.setstate() of version %s" %
                             (version, self.VERSION))

## -------------------------------------------------------
    ## ---- Methods below this point do not need to be overridden or extended
    ## ---- when subclassing for the purpose of using a different core generator.

## -------------------- pickle support  -------------------

# Issue 17489: Since __reduce__ was defined to fix #759889 this is no
    # longer called; we leave it here because it has been here since random was
    # rewritten back in 2001 and why risk breaking something.
    def __getstate__(self):  # for pickle
        return self.getstate()

def __setstate__(self, state):  # for pickle
        self.setstate(state)

def __reduce__(self):
        return self.__class__, (), self.getstate()

## ---- internal support method for evenly distributed integers ----

def __init_subclass__(cls, /, **kwargs):
        """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.
        """

for c in cls.__mro__:
            if '_randbelow' in c.__dict__:
                # just inherit it
                break
            if 'getrandbits' in c.__dict__:
                cls._randbelow = cls._randbelow_with_getrandbits
                break
            if 'random' in c.__dict__:
                cls._randbelow = cls._randbelow_without_getrandbits
                break

def _randbelow_with_getrandbits(self, n):
        "Return a random int in the range [0,n).  Returns 0 if n==0."

if not n:
            return 0
        getrandbits = self.getrandbits
        k = n.bit_length()  # don't use (n-1) here because n can be 1
        r = getrandbits(k)  # 0 <= r < 2**k
        while r >= n:
            r = getrandbits(k)
        return r

def _randbelow_without_getrandbits(self, n, maxsize=1<<BPF):
        """Return a random int in the range [0,n).  Returns 0 if n==0.

The implementation does not use getrandbits, but only random.
        """

random = self.random
        if n >= maxsize:
            _warn("Underlying random() generator does not supply \n"
                "enough bits to choose from a population range this large.\n"
                "To remove the range limitation, add a getrandbits() method.")
            return _floor(random() * n)
        if n == 0:
            return 0
        rem = maxsize % n
        limit = (maxsize - rem) / maxsize   # int(limit * maxsize) % n == 0
        r = random()
        while r >= limit:
            r = random()
        return _floor(r * maxsize) % n

_randbelow = _randbelow_with_getrandbits

## --------------------------------------------------------
    ## ---- Methods below this point generate custom distributions
    ## ---- based on the methods defined above.  They do not
    ## ---- directly touch the underlying generator and only
    ## ---- access randomness through the methods:  random(),
    ## ---- getrandbits(), or _randbelow().

## -------------------- bytes methods ---------------------

def randbytes(self, n):
        """Generate n random bytes."""
        return self.getrandbits(n * 8).to_bytes(n, 'little')

## -------------------- integer methods  -------------------

def randrange(self, start, stop=None, step=1):
        """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.

"""

# This code is a bit messy to make it fast for the
        # common case while still doing adequate error checking.
        istart = int(start)
        if istart != start:
            raise ValueError("non-integer arg 1 for randrange()")
        if stop is None:
            if istart > 0:
                return self._randbelow(istart)
            raise ValueError("empty range for randrange()")

# stop argument supplied.
        istop = int(stop)
        if istop != stop:
            raise ValueError("non-integer stop for randrange()")
        width = istop - istart
        if step == 1 and width > 0:
            return istart + self._randbelow(width)
        if step == 1:
            raise ValueError("empty range for randrange() (%d, %d, %d)" % (istart, istop, width))

# Non-unit step argument supplied.
        istep = int(step)
        if istep != step:
            raise ValueError("non-integer step for randrange()")
        if istep > 0:
            n = (width + istep - 1) // istep
        elif istep < 0:
            n = (width + istep + 1) // istep
        else:
            raise ValueError("zero step for randrange()")

if n <= 0:
            raise ValueError("empty range for randrange()")

return istart + istep * self._randbelow(n)

def randint(self, a, b):
        """Return random integer in range [a, b], including both end points.
        """

return self.randrange(a, b+1)

## -------------------- sequence methods  -------------------

def choice(self, seq):
        """Choose a random element from a non-empty sequence."""
        # raises IndexError if seq is empty
        return seq[self._randbelow(len(seq))]

def shuffle(self, x, random=None):
        """Shuffle list x in place, and return None.

Optional argument random is a 0-argument function returning a
        random float in [0.0, 1.0); if it is the default None, the
        standard random.random will be used.

"""

if random is None:
            randbelow = self._randbelow
            for i in reversed(range(1, len(x))):
                # pick an element in x[:i+1] with which to exchange x[i]
                j = randbelow(i + 1)
                x[i], x[j] = x[j], x[i]
        else:
            _warn('The *random* parameter to shuffle() has been deprecated\n'
                  'since Python 3.9 and will be removed in a subsequent '
                  'version.',
                  DeprecationWarning, 2)
            floor = _floor
            for i in reversed(range(1, len(x))):
                # pick an element in x[:i+1] with which to exchange x[i]
                j = floor(random() * (i + 1))
                x[i], x[j] = x[j], x[i]

def sample(self, population, k, *, counts=None):
        """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.

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)

"""

# Sampling without replacement entails tracking either potential
        # selections (the pool) in a list or previous selections in a set.

# When the number of selections is small compared to the
        # population, then tracking selections is efficient, requiring
        # only a small set and an occasional reselection.  For
        # a larger number of selections, the pool tracking method is
        # preferred since the list takes less space than the
        # set and it doesn't suffer from frequent reselections.

# The number of calls to _randbelow() is kept at or near k, the
        # theoretical minimum.  This is important because running time
        # is dominated by _randbelow() and because it extracts the
        # least entropy from the underlying random number generators.

# Memory requirements are kept to the smaller of a k-length
        # set or an n-length list.

# There are other sampling algorithms that do not require
        # auxiliary memory, but they were rejected because they made
        # too many calls to _randbelow(), making them slower and
        # causing them to eat more entropy than necessary.

if isinstance(population, _Set):
            _warn('Sampling from a set deprecated\n'
                  'since Python 3.9 and will be removed in a subsequent version.',
                  DeprecationWarning, 2)
            population = tuple(population)
        if not isinstance(population, _Sequence):
            raise TypeError("Population must be a sequence.  For dicts or sets, use sorted(d).")
        n = len(population)
        if counts is not None:
            cum_counts = list(_accumulate(counts))
            if len(cum_counts) != n:
                raise ValueError('The number of counts does not match the population')
            total = cum_counts.pop()
            if not isinstance(total, int):
                raise TypeError('Counts must be integers')
            if total <= 0:
                raise ValueError('Total of counts must be greater than zero')
            selections = self.sample(range(total), k=k)
            bisect = _bisect
            return [population[bisect(cum_counts, s)] for s in selections]
        randbelow = self._randbelow
        if not 0 <= k <= n:
            raise ValueError("Sample larger than population or is negative")
        result = [None] * k
        setsize = 21        # size of a small set minus size of an empty list
        if k > 5:
            setsize += 4 ** _ceil(_log(k * 3, 4))  # table size for big sets
        if n <= setsize:
            # An n-length list is smaller than a k-length set.
            # Invariant:  non-selected at pool[0 : n-i]
            pool = list(population)
            for i in range(k):
                j = randbelow(n - i)
                result[i] = pool[j]
                pool[j] = pool[n - i - 1]  # move non-selected item into vacancy
        else:
            selected = set()
            selected_add = selected.add
            for i in range(k):
                j = randbelow(n)
                while j in selected:
                    j = randbelow(n)
                selected_add(j)
                result[i] = population[j]
        return result

def choices(self, population, weights=None, *, cum_weights=None, k=1):
        """Return 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.

"""
        random = self.random
        n = len(population)
        if cum_weights is None:
            if weights is None:
                floor = _floor
                n += 0.0    # convert to float for a small speed improvement
                return [population[floor(random() * n)] for i in _repeat(None, k)]
            try:
                cum_weights = list(_accumulate(weights))
            except TypeError:
                if not isinstance(weights, int):
                    raise
                k = weights
                raise TypeError(
                    f'The number of choices must be a keyword argument: {k=}'
                ) from None
        elif weights is not None:
            raise TypeError('Cannot specify both weights and cumulative weights')
        if len(cum_weights) != n:
            raise ValueError('The number of weights does not match the population')
        total = cum_weights[-1] + 0.0   # convert to float
        if total <= 0.0:
            raise ValueError('Total of weights must be greater than zero')
        bisect = _bisect
        hi = n - 1
        return [population[bisect(cum_weights, random() * total, 0, hi)]
                for i in _repeat(None, k)]

## -------------------- real-valued distributions  -------------------

def uniform(self, a, b):
        "Get a random number in the range [a, b) or [a, b] depending on rounding."
        return a + (b - a) * self.random()

def triangular(self, low=0.0, high=1.0, mode=None):
        """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

"""
        u = self.random()
        try:
            c = 0.5 if mode is None else (mode - low) / (high - low)
        except ZeroDivisionError:
            return low
        if u > c:
            u = 1.0 - u
            c = 1.0 - c
            low, high = high, low
        return low + (high - low) * _sqrt(u * c)

def normalvariate(self, mu, sigma):
        """Normal distribution.

mu is the mean, and sigma is the standard deviation.

"""
        # Uses Kinderman and Monahan method. Reference: Kinderman,
        # A.J. and Monahan, J.F., "Computer generation of random
        # variables using the ratio of uniform deviates", ACM Trans
        # Math Software, 3, (1977), pp257-260.

random = self.random
        while True:
            u1 = random()
            u2 = 1.0 - random()
            z = NV_MAGICCONST * (u1 - 0.5) / u2
            zz = z * z / 4.0
            if zz <= -_log(u2):
                break
        return mu + z * sigma

def gauss(self, mu, sigma):
        """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.

"""
        # When x and y are two variables from [0, 1), uniformly
        # distributed, then
        #
        #    cos(2*pi*x)*sqrt(-2*log(1-y))
        #    sin(2*pi*x)*sqrt(-2*log(1-y))
        #
        # are two *independent* variables with normal distribution
        # (mu = 0, sigma = 1).
        # (Lambert Meertens)
        # (corrected version; bug discovered by Mike Miller, fixed by LM)

# Multithreading note: When two threads call this function
        # simultaneously, it is possible that they will receive the
        # same return value.  The window is very small though.  To
        # avoid this, you have to use a lock around all calls.  (I
        # didn't want to slow this down in the serial case by using a
        # lock here.)

random = self.random
        z = self.gauss_next
        self.gauss_next = None
        if z is None:
            x2pi = random() * TWOPI
            g2rad = _sqrt(-2.0 * _log(1.0 - random()))
            z = _cos(x2pi) * g2rad
            self.gauss_next = _sin(x2pi) * g2rad

return mu + z * sigma

def lognormvariate(self, 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.

"""
        return _exp(self.normalvariate(mu, sigma))

def expovariate(self, 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.

"""
        # lambd: rate lambd = 1/mean
        # ('lambda' is a Python reserved word)

# we use 1-random() instead of random() to preclude the
        # possibility of taking the log of zero.
        return -_log(1.0 - self.random()) / lambd

def vonmisesvariate(self, mu, kappa):
        """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.

"""
        # Based upon an algorithm published in: Fisher, N.I.,
        # "Statistical Analysis of Circular Data", Cambridge
        # University Press, 1993.

# Thanks to Magnus Kessler for a correction to the
        # implementation of step 4.

random = self.random
        if kappa <= 1e-6:
            return TWOPI * random()

s = 0.5 / kappa
        r = s + _sqrt(1.0 + s * s)

while True:
            u1 = random()
            z = _cos(_pi * u1)

d = z / (r + z)
            u2 = random()
            if u2 < 1.0 - d * d or u2 <= (1.0 - d) * _exp(d):
                break

q = 1.0 / r
        f = (q + z) / (1.0 + q * z)
        u3 = random()
        if u3 > 0.5:
            theta = (mu + _acos(f)) % TWOPI
        else:
            theta = (mu - _acos(f)) % TWOPI

return theta

def gammavariate(self, 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

"""
        # alpha > 0, beta > 0, mean is alpha*beta, variance is alpha*beta**2

# Warning: a few older sources define the gamma distribution in terms
        # of alpha > -1.0
        if alpha <= 0.0 or beta <= 0.0:
            raise ValueError('gammavariate: alpha and beta must be > 0.0')

random = self.random
        if alpha > 1.0:

# Uses R.C.H. Cheng, "The generation of Gamma
            # variables with non-integral shape parameters",
            # Applied Statistics, (1977), 26, No. 1, p71-74

ainv = _sqrt(2.0 * alpha - 1.0)
            bbb = alpha - LOG4
            ccc = alpha + ainv

while 1:
                u1 = random()
                if not 1e-7 < u1 < 0.9999999:
                    continue
                u2 = 1.0 - random()
                v = _log(u1 / (1.0 - u1)) / ainv
                x = alpha * _exp(v)
                z = u1 * u1 * u2
                r = bbb + ccc * v - x
                if r + SG_MAGICCONST - 4.5 * z >= 0.0 or r >= _log(z):
                    return x * beta

elif alpha == 1.0:
            # expovariate(1/beta)
            return -_log(1.0 - random()) * beta

else:
            # alpha is between 0 and 1 (exclusive)
            # Uses ALGORITHM GS of Statistical Computing - Kennedy & Gentle
            while True:
                u = random()
                b = (_e + alpha) / _e
                p = b * u
                if p <= 1.0:
                    x = p ** (1.0 / alpha)
                else:
                    x = -_log((b - p) / alpha)
                u1 = random()
                if p > 1.0:
                    if u1 <= x ** (alpha - 1.0):
                        break
                elif u1 <= _exp(-x):
                    break
            return x * beta

def betavariate(self, alpha, beta):
        """Beta distribution.

Conditions on the parameters are alpha > 0 and beta > 0.
        Returned values range between 0 and 1.

"""
        ## See
        ## http://mail.python.org/pipermail/python-bugs-list/2001-January/003752.html
        ## for Ivan Frohne's insightful analysis of why the original implementation:
        ##
        ##    def betavariate(self, alpha, beta):
        ##        # Discrete Event Simulation in C, pp 87-88.
        ##
        ##        y = self.expovariate(alpha)
        ##        z = self.expovariate(1.0/beta)
        ##        return z/(y+z)
        ##
        ## was dead wrong, and how it probably got that way.

# This version due to Janne Sinkkonen, and matches all the std
        # texts (e.g., Knuth Vol 2 Ed 3 pg 134 "the beta distribution").
        y = self.gammavariate(alpha, 1.0)
        if y:
            return y / (y + self.gammavariate(beta, 1.0))
        return 0.0

def paretovariate(self, alpha):
        """Pareto distribution.  alpha is the shape parameter."""
        # Jain, pg. 495

u = 1.0 - self.random()
        return 1.0 / u ** (1.0 / alpha)

def weibullvariate(self, alpha, beta):
        """Weibull distribution.

alpha is the scale parameter and beta is the shape parameter.

"""
        # Jain, pg. 499; bug fix courtesy Bill Arms

u = 1.0 - self.random()
        return alpha * (-_log(u)) ** (1.0 / beta)

## ------------------------------------------------------------------
## --------------- Operating System Random Source  ------------------

class SystemRandom(Random):
    """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).

"""

def random(self):
        """Get the next random number in the range [0.0, 1.0)."""
        return (int.from_bytes(_urandom(7), 'big') >> 3) * RECIP_BPF

def getrandbits(self, k):
        """getrandbits(k) -> x.  Generates an int with k random bits."""
        if k < 0:
            raise ValueError('number of bits must be non-negative')
        numbytes = (k + 7) // 8                       # bits / 8 and rounded up
        x = int.from_bytes(_urandom(numbytes), 'big')
        return x >> (numbytes * 8 - k)                # trim excess bits

def randbytes(self, n):
        """Generate n random bytes."""
        # os.urandom(n) fails with ValueError for n < 0
        # and returns an empty bytes string for n == 0.
        return _urandom(n)

def seed(self, *args, **kwds):
        "Stub method.  Not used for a system random number generator."
        return None

def _notimplemented(self, *args, **kwds):
        "Method should not be called for a system random number generator."
        raise NotImplementedError('System entropy source does not have state.')
    getstate = setstate = _notimplemented

# ----------------------------------------------------------------------
# Create one instance, seeded from current time, and export its methods
# as module-level functions.  The functions share state across all uses
# (both in the user's code and in the Python libraries), but that's fine
# for most programs and is easier for the casual user than making them
# instantiate their own Random() instance.

_inst = Random()
seed = _inst.seed
random = _inst.random
uniform = _inst.uniform
triangular = _inst.triangular
randint = _inst.randint
choice = _inst.choice
randrange = _inst.randrange
sample = _inst.sample
shuffle = _inst.shuffle
choices = _inst.choices
normalvariate = _inst.normalvariate
lognormvariate = _inst.lognormvariate
expovariate = _inst.expovariate
vonmisesvariate = _inst.vonmisesvariate
gammavariate = _inst.gammavariate
gauss = _inst.gauss
betavariate = _inst.betavariate
paretovariate = _inst.paretovariate
weibullvariate = _inst.weibullvariate
getstate = _inst.getstate
setstate = _inst.setstate
getrandbits = _inst.getrandbits
randbytes = _inst.randbytes

## ------------------------------------------------------
## ----------------- test program -----------------------

def _test_generator(n, func, args):
    from statistics import stdev, fmean as mean
    from time import perf_counter

t0 = perf_counter()
    data = [func(*args) for i in range(n)]
    t1 = perf_counter()

xbar = mean(data)
    sigma = stdev(data, xbar)
    low = min(data)
    high = max(data)

print(f'{t1 - t0:.3f} sec, {n} times {func.__name__}')
    print('avg %g, stddev %g, min %g, max %g\n' % (xbar, sigma, low, high))

def _test(N=2000):
    _test_generator(N, random, ())
    _test_generator(N, normalvariate, (0.0, 1.0))
    _test_generator(N, lognormvariate, (0.0, 1.0))
    _test_generator(N, vonmisesvariate, (0.0, 1.0))
    _test_generator(N, gammavariate, (0.01, 1.0))
    _test_generator(N, gammavariate, (0.1, 1.0))
    _test_generator(N, gammavariate, (0.1, 2.0))
    _test_generator(N, gammavariate, (0.5, 1.0))
    _test_generator(N, gammavariate, (0.9, 1.0))
    _test_generator(N, gammavariate, (1.0, 1.0))
    _test_generator(N, gammavariate, (2.0, 1.0))
    _test_generator(N, gammavariate, (20.0, 1.0))
    _test_generator(N, gammavariate, (200.0, 1.0))
    _test_generator(N, gauss, (0.0, 1.0))
    _test_generator(N, betavariate, (3.0, 3.0))
    _test_generator(N, triangular, (0.0, 1.0, 1.0 / 3.0))

## ------------------------------------------------------
## ------------------ fork support  ---------------------

if hasattr(_os, "fork"):
    _os.register_at_fork(after_in_child=_inst.seed)

if __name__ == '__main__':
    _test()

cmd: