"""Random variable generators.
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.
"""
from warnings import warn as _warn
from types import MethodType as _MethodType, BuiltinMethodType as _BuiltinMethodType
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 os import urandom as _urandom
from _collections_abc import Set as _Set, Sequence as _Sequence
from hashlib import sha512 as _sha512
import itertools as _itertools
import bisect as _bisect
__all__ = ["Random","seed","random","uniform","randint","choice","sample",
"randrange","shuffle","normalvariate","lognormvariate",
"expovariate","vonmisesvariate","gammavariate","triangular",
"gauss","betavariate","paretovariate","weibullvariate",
"getstate","setstate", "getrandbits", "choices",
"SystemRandom"]
NV_MAGICCONST = 4 * _exp(-0.5)/_sqrt(2.0)
TWOPI = 2.0*_pi
LOG4 = _log(4.0)
SG_MAGICCONST = 1.0 + _log(4.5)
BPF = 53 # Number of bits in a float
RECIP_BPF = 2**-BPF
# 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.
import _random
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 hashable object.
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
if version == 2 and isinstance(a, (str, bytes, bytearray)):
if isinstance(a, str):
a = a.encode()
a += _sha512(a).digest()
a = int.from_bytes(a, 'big')
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 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()
## -------------------- integer methods -------------------
def randrange(self, start, stop=None, step=1, _int=int):
"""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)
def _randbelow(self, n, int=int, maxsize=1<<BPF, type=type,
Method=_MethodType, BuiltinMethod=_BuiltinMethodType):
"Return a random int in the range [0,n). Raises ValueError if n==0."
random = self.random
getrandbits = self.getrandbits
# Only call self.getrandbits if the original random() builtin method
# has not been overridden or if a new getrandbits() was supplied.
if type(random) is BuiltinMethod or type(getrandbits) is Method:
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
# There's an overridden random() method but no new getrandbits() method,
# so we can only use random() from here.
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 int(random() * n)
if n == 0:
raise ValueError("Boundary cannot be zero")
rem = maxsize % n
limit = (maxsize - rem) / maxsize # int(limit * maxsize) % n == 0
r = random()
while r >= limit:
r = random()
return int(r*maxsize) % n
## -------------------- sequence methods -------------------
def choice(self, seq):
"""Choose a random element from a non-empty sequence."""
try:
i = self._randbelow(len(seq))
except ValueError:
raise IndexError('Cannot choose from an empty sequence') from None
return seq[i]
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:
_int = int
for i in reversed(range(1, len(x))):
# pick an element in x[:i+1] with which to exchange x[i]
j = _int(random() * (i+1))
x[i], x[j] = x[j], x[i]
def sample(self, population, k):
"""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)
"""
# 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.
if isinstance(population, _Set):
population = tuple(population)
if not isinstance(population, _Sequence):
raise TypeError("Population must be a sequence or set. For dicts, use list(d).")
randbelow = self._randbelow
n = len(population)
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
pool = list(population)
for i in range(k): # invariant: non-selected at [0,n-i)
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
if cum_weights is None:
if weights is None:
_int = int
total = len(population)
return [population[_int(random() * total)] for i in range(k)]
cum_weights = list(_itertools.accumulate(weights))
elif weights is not None:
raise TypeError('Cannot specify both weights and cumulative weights')
if len(cum_weights) != len(population):
raise ValueError('The number of weights does not match the population')
bisect = _bisect.bisect
total = cum_weights[-1]
hi = len(cum_weights) - 1
return [population[bisect(cum_weights, random() * total, 0, hi)]
for i in range(k)]
## -------------------- real-valued distributions -------------------
## -------------------- uniform distribution -------------------
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()
## -------------------- triangular --------------------
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) * (u * c) ** 0.5
## -------------------- normal distribution --------------------
def normalvariate(self, mu, sigma):
"""Normal distribution.
mu is the mean, and sigma is the standard deviation.
"""
# mu = mean, sigma = 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 1:
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
## -------------------- lognormal distribution --------------------
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))
## -------------------- exponential distribution --------------------
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
## -------------------- von Mises distribution --------------------
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.
"""
# mu: mean angle (in radians between 0 and 2*pi)
# kappa: concentration parameter kappa (>= 0)
# if kappa = 0 generate uniform random angle
# 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 1:
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
## -------------------- gamma distribution --------------------
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 < .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)
u = random()
while u <= 1e-7:
u = random()
return -_log(u) * beta
else: # alpha is between 0 and 1 (exclusive)
# Uses ALGORITHM GS of Statistical Computing - Kennedy & Gentle
while 1:
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
## -------------------- Gauss (faster alternative) --------------------
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
## -------------------- beta --------------------
## 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.
def betavariate(self, alpha, beta):
"""Beta distribution.
Conditions on the parameters are alpha > 0 and beta > 0.
Returned values range between 0 and 1.
"""
# 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 == 0:
return 0.0
else:
return y / (y + self.gammavariate(beta, 1.0))
## -------------------- Pareto --------------------
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)
## -------------------- Weibull --------------------
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 greater than zero')
if k != int(k):
raise TypeError('number of bits should be an integer')
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 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
## -------------------- test program --------------------
def _test_generator(n, func, args):
import time
print(n, 'times', func.__name__)
total = 0.0
sqsum = 0.0
smallest = 1e10
largest = -1e10
t0 = time.time()
for i in range(n):
x = func(*args)
total += x
sqsum = sqsum + x*x
smallest = min(x, smallest)
largest = max(x, largest)
t1 = time.time()
print(round(t1-t0, 3), 'sec,', end=' ')
avg = total/n
stddev = _sqrt(sqsum/n - avg*avg)
print('avg %g, stddev %g, min %g, max %g\n' % \
(avg, stddev, smallest, largest))
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))
# 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
if __name__ == '__main__':
_test()
I am a small town Minnesota single mom of two great kids who are my life. I began modeling 3 years ago for a photographer ho saw something in me I never did. Ice told me I should do one shoot with him and let that be the guide. I reluctantly agreed, and scheduled our date. I was sacred to death when he told me we would be doing a remake of the publicity stills of the 1956 movie Bus Stop, staring Marilyn Monroe. How in the world could I halfway resemble or pull off an icon the likes of Marylin Monroe in my first step in front of camera? Well, 2 hours later we had a nice set of images and I've been hooked ever since. We've done some really cool things and are looking hard at the future ahead to expand and get me out there a little more.
My pinup journey started at the age of 13 when I started collecting vintage decor and clothing- it has since spiraled into doing pinup shoots, meeting and developing friendships with other gorgeous pinups and being published in a pinup blog and magazine. Looking forward to the future and to see where other opportunities will take me!
Full Bio
I started getting into collecting vintage when I was a young kid, my mom would always take me into antique stores and this seemed to be what fueled it all. Eventually I started dressing and collecting vintage clothing and home decor. My apartment is now a great mix of MCM. I’ve done several pinup photoshoots and am looking to doing more in the future. I have been featured in a online pinup blog as well as being published in an state content creators magazine. Looking forward to the future and all the adventures it brings going forward.
Meet Belle Starr, your favorite tattooed 💉, curvy 💃 nurse turning heads and stealing hearts 💘 across Northwest Florida. A professional nurse 👩⚕️ during the week and a sultry pinup queen 👑 on the weekends, she’s the ultimate blend of classy ✨ and sassy 🔥—a vintage vixen with a modern twist.
Full Bio
Meet Belle Starr, your favorite tattooed 💉, curvy 💃 nurse turning heads and stealing hearts 💘 across Northwest Florida. A professional nurse 👩⚕️ during the week and a sultry pinup queen 👑 on the weekends, she’s the ultimate blend of classy ✨ and sassy 🔥—a vintage vixen with a modern twist.
She serves as the secretary for Pinups and Pumps Florida Chapter 💄 and is the official correspondent for PinupDatabase.com 🖋️. Belle Starr is dedicated to empowering women 👠, spotlighting the pinup community, and keeping the spirit of pinup history alive 📸. When she’s not hostessing 🎤 or interviewing at events 🌟, she’s a fierce advocate for the Ostel Place Foundation 🐴🐶🌿, a charity that helps people heal through horses, puppies, and the beauty of nature.
Whether she’s inspiring women 💋, enticing men 🕶️, or stealing the show as an event hostess 🎉, Belle Starr proves that beauty 💎, brains 🧠, and curves 🔥 never go out of style. Follow her journey for a dose of entertainment 🎭, empowerment 💪, and unforgettable vibes 🌟.
I'm a Pin Up model, classic car lover and Patriot. Been in Pin Up since 2014.
Full Bio
BoomBoom Bettie has been in the pinup world since 2014. She has participated in pageants in person and online since 2019. She loves the title of Favorite Pearl that she received. She is the founder of a Pin Up club called Black Sheep Pin Up Social Club in Arizona. She loves being a part of the pin up world and the sisterhood it creates. She loves to attend local car shows and Pin Up events.
𝑰 𝒑𝒐𝒔𝒕 my own pics, 𝒂𝒍𝒍 𝒄𝒍𝒂𝒔𝒔𝒚, 𝒖𝒔𝒖𝒂𝒍𝒍𝒚 𝒘𝒊𝒕𝒉 𝒇𝒖𝒏 𝒕𝒉𝒆𝒎𝒆𝒔. 𝑪𝒖𝒔𝒕𝒐𝒎 𝒘𝒐𝒓𝒌 𝒊𝒔 𝒂𝒗𝒂𝒊𝒍𝒂𝒃𝒍𝒆.
Jill of All, Owner of 5.
@currentteevents philanthropic tshirts
@shopcadesigns jewelry
@ciaraandruby dog models
@openmybar bar consulting
@calishamrock art/photography
My awesome journey began in California, followed by 25 wonderful years in Colorado. In 2019 I made the best choice of my life—moving to Florida, where I’ve truly found my home. The pin up community has been amazing, as I have always been drawn to the vibrant world of rockabilly style, classic cars, and music. Known for being kind, generous, and full of adventure, I cherish my experiences and connecting with new people. As a proud member of "Pinups and Pumps," I deeply appreciate the camaraderie with my sisters. Together, we give back through charity events, creating lasting bonds and memories.
Rating (average)
(0)
City
St. Augustine
Province
FL
Pin Up Group Membership
Pinups and Pumps Florida
Published in the Following Publications
Dream Beauty, Dream Pinup, Wonderland, Social Pin, Smitten Kitten, Dollface Digest, Crowns & Chrome, Drive In and many more
Clarice entered the pinup scene officially in 2019. Her first photoshoot was a tribute to the queen herself, Bettie Page. Dawning the same iconic bangs and hair darker than the devil's soul, she was a tattooed dead ringer. That photoshoot was featured in Retro Lovely's Bettie Page issue in 2019.
6 years later Clarice is a style of her own, finding herself more and more every day. She's a mental health advocate, constantly trying to educate about mental illness to help end the stigma. In March of this year she'll be celebrating 3 years free from alcohol. Supporting sobriety amongst her community is also a passion. Clarice is also Autistic, and tries to educate on hidden disabilities. Not only is she a pinup, she's a mommy first. Having 3 biological children, 3 "step"children, and her youngest being adopted, who's also autistic.
She enjoys creating art through painting, drawing, photography, and floral hair pieces.
Find her at the car shows, especially if there are rat rods and lowriders involved. Lowriders have been a part of her heart since high school. From being in a friend's hopper getting Taco Bell past her curfew, or cruising the beach with the systems bumping.
The name Clarice Von Darling is a tribute to The Silence of the Lambs. In her sister's memory.
62 year old trans woman who is now retired and living life to the fullest. Many past careers including dairy farmer firefighter/emt truck driver school bus driver church sexton cemetery sexton Public works director juice company truck driver and over the road truck driver. Two grown adult children ages 36 and 33 Two grand children ages 14 and 4 Local church member
