Pyewacket 1.3.1

Drop-in Replacement for the Python Random Module.

Pyewacket

Fast, fault-tolerant, drop-in replacement for the Python3 random module

Built on top of the RNG Storm Engine for stability and performance. While Storm is a high quality random engine, Pyewacket is not appropriate for cryptography of any kind. Pyewacket is meant for games, data science, A.I. and experimental programming, not security.

Sister Projects:

• Fortuna: Collection of tools to make custom random value generators. https://pypi.org/project/Fortuna/
• Pyewacket: Drop-in replacement for the Python3 random module. https://pypi.org/project/Pyewacket/
• MonkeyScope: Framework for testing non-deterministic value generators. https://pypi.org/project/MonkeyScope/

Support these and other random projects: https://www.patreon.com/brokencode

Quick Install $ pip install Pyewacket

Installation may require the following:

• Python 3.7 or later with dev tools (setuptools, pip, etc.)
• Cython: pip install Cython
• Modern C++17 Compiler and Standard Library.

Random Generators

Random Integers

• Pyewacket.randbelow(n: int) -> int
  • While randrange(a, b, c) can be handy, it's more complex than needed most of the time. Mathematically, randbelow(n) is equivalent to randrange(n) and they have nearly the same performance characteristics in Pyewacket, 10x - 12x faster than the random module's internal randbelow().
  • @param n :: Pyewacket expands the acceptable input domain to include non-positive values of n.
  • @return :: random integer in range (n, 0] or [0, n) depending on the sign of n.

from Pyewacket import randbelow


""" Standard """
randbelow(10)       # -> [0, 10)

""" Extras """
randbelow(0)        # -> [0, 0) => 0
randbelow(-10)      # -> (-10, 0]

• Pyewacket.randint(a: int, b: int) -> int
  • @param a, b :: both are required,
  • @return :: random integer in range [a, b] or [b, a]
  • Inclusive on both sides
  • Removed the asymmetric requirement of a < b
  • When a == b returns a

from Pyewacket import randint


""" Standard """
randint(1, 10)      # -> [1, 10]

""" Extras """
randint(10, 1)      # -> [1, 10]
randint(10, 10)     # -> [10, 10] => 10

• Pyewacket.randrange(start: int, stop: int = 0, step: int = 1) -> int
  • Fault tolerant and about 20x faster than random.randrange()
  • @param start :: required
  • @param stop :: optional, default=0
  • @parma step :: optional, default=1
  • @return :: random integer in range (stop, start] or [start, stop) by |step|
  • Removed the requirements of start < stop, and step > 0
  • Always returns start for start == stop or step == 0

from Pyewacket import randrange


""" Standard """
randrange(10)           # -> [0, 10) by whole numbers
randrange(1, 10)        # -> [1, 10) by whole numbers
randrange(1, 10, 2)     # -> [1, 10) by 2, odd numbers

""" Extras """
randrange(-10)          # -> [-10, 0) by 1
randrange(10, 1)        # -> [1, 10) by 1
randrange(10, 0, 2)     # -> [0, 10) by 2, even numbers
randrange(10, 10, 0)    # -> [10, 10) => 10

Random Floating Point

• Pyewacket.random() -> float
  • random float in range [0.0, 1.0] or [0.0, 1.0) depending on rounding.
  • This is the only function that doesn't show a performance increase, as expected.
  • Roughly the same speed as random.random()
• Pyewacket.uniform(a: float, b: float) -> float
  • random float in [a, b] or [a, b) depending on rounding
  • 4x faster
• Pyewacket.expovariate(lambd: float) -> float
  • 5x faster
• Pyewacket.gammavariate(alpha, beta) -> float
  • 10x faster
• Pyewacket.weibullvariate(alpha, beta) -> float
  • 4x faster
• Pyewacket.betavariate(alpha, beta) -> float
  • 16x faster
• Pyewacket.paretovariate(alpha) -> float
  • 4x faster
• Pyewacket.gauss(mu: float, sigma: float) -> float
  • 10x faster
• Pyewacket.normalvariate(mu: float, sigma: float) -> float
  • 10x faster
• Pyewacket.lognormvariate(mu: float, sigma: float) -> float
  • 10x faster
• Pyewacket.vonmisesvariate(mu: float, kappa: float) -> float
  • 4x faster
• Pyewacket.triangular(low: float, high: float, mode: float = None)
  • 10x faster

Random Sequence Values

• Pyewacket.choice(seq: List) -> Value
  • An order of magnitude faster than random.choice().
  • @param seq :: any zero indexed object like a list or tuple.
  • @return :: random value from the list, can be any object type that can be put into a list.
• Pyewacket.choices(population, weights=None, *, cum_weights=None, k=1)
  • @param population :: data values
  • @param weights :: relative weights
  • @param cum_weights :: cumulative weights
  • @param k :: number of samples to be collected
  • Only seeing a 2x performance gain.
• Pyewacket.cumulative_weighted_choice(table, k=1)
  • 10x faster than choices, but radically different API and a bit less flexible.
  • Supports Cumulative Weights only. Convert relative weights to cumulative if needed: cum_weights = tuple(itertools.accumulate(rel_weights))
  • @param table :: two dimensional list or tuple of weighted value pairs. [(1, "a"), (10, "b"), (100, "c")...]
    • The table can be constructed as tuple(zip(cum_weights, population)) weights always come first.
  • @param k :: number of samples to be collected. Returns a list of size k if k > 1, otherwise returns a single value - not a list of one.
• Pyewacket.shuffle(array: list) -> None
  • Shuffles a list in place.
  • @param array :: must be a mutable list.
  • Approximately 20 times faster than random.shuffle().
  • Implements Knuth B Shuffle Algorithm. Knuth B is twice as fast as Knuth A or Fisher-Yates for every test case. This is likely due to the combination of walking backward and rotating backward into the back side of the list. With this combination it can never modify the data it still needs to walk through. Fresh snow all the way home, aka very low probability for cache misses.
• Pyewacket.sample(population: List, k: int) -> list
  • @param population :: list or tuple.
  • @param k :: number of unique samples to get.
  • @return :: size k list of unique random samples.
  • Performance gains range (5x to 20x) depending on len(population) and the ratio of k to len(population). Higher performance gains are seen when k ~= pop size.

Hardware & Software Seeding

• seed(seed: int=0) -> None
  • Hardware seeding is enabled by default. This function is used to turn toggle software seeding and set or reset the engine seed. This affects all random functions in the module.
  • @param seed :: any non-zero positive integer less than 2**63 enables software seeding.
  • Calling seed() or seed(0) will turn off software seeding and re-enable hardware seeding.
  • While you can toggle software seeding on and off and re-seed the engine at will without error, this function is not intended or optimized to be used inside a loop. General rule: seed once, or better yet, not at all. Typically, software seeding is for debugging a product, hardware seeding is used for product release.

Development Log

Pyewacket 1.3.1

• Storm Update

Pyewacket 1.3.0

• Major API Update, several utilities have been moved into their own module: MonkeyScope.
  • distribution_timer
  • distribution
  • timer

Pyewacket 1.2.4

• Pyewacket.randrange() bug fix
• Test Update

Pyewacket 1.2.3

• Minor Bug Fix

Pyewacket 1.2.2

• Typo Fix

Pyewacket 1.2.1

• Test Update

Pyewacket 1.2.0

• Storm Update

Pyewacket 1.1.2

• Low level clean up

Pyewacket 1.1.1

• Docs Update

Pyewacket 1.1.0

• Storm Engine Update

Pyewacket 1.0.3

• minor typos

Pyewacket 1.0.2

• added choices alternative cumulative_weighted_choice

Pyewacket 1.0.1

• minor typos

Pyewacket 1.0.0

• Storm 2 Rebuild.

Pyewacket 0.1.22

• Small bug fix.

Pyewacket 0.1.21

• Public Release

Pyewacket 0.0.2b1

• Added software seeding.

Pyewacket v0.0.1b8

• Fixed a small bug in the tests.

Pyewacket v0.0.1b7

• Engine Fine Tuning
• Fixed some typos.

Pyewacket v0.0.1b6

• Rearranged tests to be more consistent and match the documentation.

Pyewacket v0.0.1b5

• Documentation Upgrade
• Minor Performance Tweaks

Pyewacket v0.0.1b4

• Public Beta

Pyewacket v0.0.1b3

• quick_test()
• Extended Functionality
  • sample()
  • expovariate()
  • gammavariate()
  • weibullvariate()
  • betavariate()
  • paretovariate()
  • gauss()
  • normalvariate()
  • lognormvariate()
  • vonmisesvariate()
  • triangular()

Pyewacket v0.0.1b2

• Basic Functionality
  • random()
  • uniform()
  • randbelow()
  • randint()
  • randrange()
  • choice()
  • choices()
  • shuffle()

Pyewacket v0.0.1b1

• Initial Design & Planning

Distribution and Performance Tests

MonkeyScope: Pyewacket

Base Case
Output Analysis: Random._randbelow(10)
Typical Timing: 625 ± 26 ns
Statistics of 1024 samples:
 Minimum: 0
 Median: 5
 Maximum: 9
 Mean: 4.798828125
 Std Deviation: 2.8412818059514775
Distribution of 10000 samples:
 0: 9.59%
 1: 9.59%
 2: 10.0%
 3: 9.66%
 4: 9.96%
 5: 10.16%
 6: 10.64%
 7: 10.54%
 8: 9.97%
 9: 9.89%

Output Analysis: randbelow(10)
Typical Timing: 63 ± 16 ns
Statistics of 1024 samples:
 Minimum: 0
 Median: 4
 Maximum: 9
 Mean: 4.4736328125
 Std Deviation: 2.87730324858167
Distribution of 10000 samples:
 0: 10.56%
 1: 9.49%
 2: 9.88%
 3: 9.5%
 4: 9.74%
 5: 10.26%
 6: 10.26%
 7: 10.24%
 8: 10.02%
 9: 10.05%

Base Case
Output Analysis: Random.randint(1, 10)
Typical Timing: 1188 ± 28 ns
Statistics of 1024 samples:
 Minimum: 1
 Median: 6
 Maximum: 10
 Mean: 5.533203125
 Std Deviation: 2.8306504048222956
Distribution of 10000 samples:
 1: 9.82%
 2: 10.0%
 3: 10.04%
 4: 9.99%
 5: 10.1%
 6: 9.85%
 7: 10.17%
 8: 10.29%
 9: 9.77%
 10: 9.97%

Output Analysis: randint(1, 10)
Typical Timing: 63 ± 14 ns
Statistics of 1024 samples:
 Minimum: 1
 Median: 5
 Maximum: 10
 Mean: 5.4384765625
 Std Deviation: 2.8484229354840367
Distribution of 10000 samples:
 1: 10.36%
 2: 10.16%
 3: 9.74%
 4: 10.08%
 5: 10.24%
 6: 9.9%
 7: 10.41%
 8: 9.68%
 9: 9.34%
 10: 10.09%

Base Case
Output Analysis: Random.randrange(0, 10, 2)
Typical Timing: 1313 ± 26 ns
Statistics of 1024 samples:
 Minimum: 0
 Median: 4
 Maximum: 8
 Mean: 4.1328125
 Std Deviation: 2.800282331108748
Distribution of 10000 samples:
 0: 19.39%
 2: 20.74%
 4: 19.34%
 6: 20.65%
 8: 19.88%

Output Analysis: randrange(0, 10, 2)
Typical Timing: 94 ± 8 ns
Statistics of 1024 samples:
 Minimum: 0
 Median: 4
 Maximum: 8
 Mean: 4.078125
 Std Deviation: 2.9104843009506434
Distribution of 10000 samples:
 0: 19.67%
 2: 20.28%
 4: 19.51%
 6: 20.58%
 8: 19.96%

Base Case
Output Analysis: Random.random()
Typical Timing: 32 ± 16 ns
Statistics of 1024 samples:
 Minimum: 4.242306496871073e-06
 Median: (0.49741199340289866, 0.49960877356802635)
 Maximum: 0.999900134037655
 Mean: 0.5023309078559874
 Std Deviation: 0.29027027669322697
Post-processor distribution of 10000 samples using round method:
 0: 50.0%
 1: 50.0%

Output Analysis: random()
Typical Timing: 32 ± 16 ns
Statistics of 1024 samples:
 Minimum: 0.0004229272055438161
 Median: (0.489088643721994, 0.49003556302279366)
 Maximum: 0.9972751124812951
 Mean: 0.49511992889136774
 Std Deviation: 0.29468065770886476
Post-processor distribution of 10000 samples using round method:
 0: 49.07%
 1: 50.93%

Base Case
Output Analysis: Random.uniform(0.0, 10.0)
Typical Timing: 219 ± 15 ns
Statistics of 1024 samples:
 Minimum: 0.0034067516057856295
 Median: (4.845508927043113, 4.855334088077171)
 Maximum: 9.99700576747118
 Mean: 4.916275927838908
 Std Deviation: 2.8445109781758453
Post-processor distribution of 10000 samples using floor method:
 0: 10.12%
 1: 9.68%
 2: 9.48%
 3: 10.0%
 4: 10.54%
 5: 9.79%
 6: 10.27%
 7: 10.15%
 8: 10.48%
 9: 9.49%

Output Analysis: uniform(0.0, 10.0)
Typical Timing: 32 ± 15 ns
Statistics of 1024 samples:
 Minimum: 0.0021605864762300085
 Median: (4.92784171701383, 4.9284043089777825)
 Maximum: 9.997175886453395
 Mean: 5.047256073750533
 Std Deviation: 2.872636203746805
Post-processor distribution of 10000 samples using floor method:
 0: 9.69%
 1: 9.95%
 2: 9.19%
 3: 10.21%
 4: 10.3%
 5: 10.02%
 6: 10.13%
 7: 10.3%
 8: 9.65%
 9: 10.56%

Base Case
Output Analysis: Random.expovariate(1.0)
Typical Timing: 313 ± 10 ns
Statistics of 1024 samples:
 Minimum: 0.00015261939852430782
 Median: (0.6767337187639765, 0.6776024361595817)
 Maximum: 8.884703122888563
 Mean: 1.0056325974850917
 Std Deviation: 1.0142273680637475
Post-processor distribution of 10000 samples using floor method:
 0: 62.61%
 1: 23.39%
 2: 8.96%
 3: 3.15%
 4: 1.21%
 5: 0.46%
 6: 0.15%
 7: 0.03%
 8: 0.04%

Output Analysis: expovariate(1.0)
Typical Timing: 63 ± 1 ns
Statistics of 1024 samples:
 Minimum: 0.0003552094568829111
 Median: (0.6922546291029696, 0.6949046016507148)
 Maximum: 7.816841884508014
 Mean: 0.9957510217561668
 Std Deviation: 0.9867200542788259
Post-processor distribution of 10000 samples using floor method:
 0: 63.5%
 1: 22.64%
 2: 8.77%
 3: 3.29%
 4: 1.09%
 5: 0.42%
 6: 0.21%
 7: 0.05%
 8: 0.02%
 12: 0.01%

Base Case
Output Analysis: Random.gammavariate(2.0, 1.0)
Typical Timing: 1188 ± 63 ns
Statistics of 1024 samples:
 Minimum: 0.029064126539252052
 Median: (1.7356141898023245, 1.7432217196522695)
 Maximum: 9.1826490758087
 Mean: 2.0498521757684993
 Std Deviation: 1.4422872135863132
Post-processor distribution of 10000 samples using round method:
 0: 8.83%
 1: 35.17%
 2: 26.99%
 3: 14.87%
 4: 7.8%
 5: 3.43%
 6: 1.62%
 7: 0.72%
 8: 0.25%
 9: 0.22%
 10: 0.06%
 11: 0.02%
 13: 0.02%

Output Analysis: gammavariate(2.0, 1.0)
Typical Timing: 94 ± 14 ns
Statistics of 1024 samples:
 Minimum: 0.03570136988527761
 Median: (1.7750888964893237, 1.7837389139202866)
 Maximum: 9.30342913528465
 Mean: 2.1188685863241044
 Std Deviation: 1.5195606332383071
Post-processor distribution of 10000 samples using round method:
 0: 8.65%
 1: 35.26%
 2: 26.76%
 3: 15.27%
 4: 7.76%
 5: 3.31%
 6: 1.49%
 7: 0.86%
 8: 0.39%
 9: 0.16%
 10: 0.06%
 11: 0.01%
 12: 0.02%

Base Case
Output Analysis: Random.weibullvariate(1.0, 1.0)
Typical Timing: 407 ± 14 ns
Statistics of 1024 samples:
 Minimum: 0.003461464842162897
 Median: (0.6933857961661418, 0.6941479109967058)
 Maximum: 6.129947381154091
 Mean: 0.9883521658906127
 Std Deviation: 0.9594784870208136
Post-processor distribution of 10000 samples using floor method:
 0: 63.36%
 1: 23.18%
 2: 8.45%
 3: 3.27%
 4: 1.28%
 5: 0.34%
 6: 0.07%
 7: 0.02%
 8: 0.02%
 9: 0.01%

Output Analysis: weibullvariate(1.0, 1.0)
Typical Timing: 94 ± 11 ns
Statistics of 1024 samples:
 Minimum: 0.0013745586112774827
 Median: (0.6755198586571766, 0.6796722649264857)
 Maximum: 6.0102905499581345
 Mean: 0.9827281322098893
 Std Deviation: 0.947483782009943
Post-processor distribution of 10000 samples using floor method:
 0: 62.68%
 1: 24.09%
 2: 8.17%
 3: 3.26%
 4: 1.18%
 5: 0.37%
 6: 0.14%
 7: 0.09%
 8: 0.01%
 9: 0.01%

Base Case
Output Analysis: Random.betavariate(3.0, 3.0)
Typical Timing: 2500 ± 95 ns
Statistics of 1024 samples:
 Minimum: 0.017148095728798357
 Median: (0.4865970971692021, 0.48665029043734914)
 Maximum: 0.9533418414254362
 Mean: 0.49387959798109937
 Std Deviation: 0.1911014803178371
Post-processor distribution of 10000 samples using round method:
 0: 50.55%
 1: 49.45%

Output Analysis: betavariate(3.0, 3.0)
Typical Timing: 188 ± 14 ns
Statistics of 1024 samples:
 Minimum: 0.032571853153678616
 Median: (0.4913446798888493, 0.4916242700105953)
 Maximum: 0.9725517100360436
 Mean: 0.5028801394037684
 Std Deviation: 0.18907189324542073
Post-processor distribution of 10000 samples using round method:
 0: 49.72%
 1: 50.28%

Base Case
Output Analysis: Random.paretovariate(4.0)
Typical Timing: 282 ± 14 ns
Statistics of 1024 samples:
 Minimum: 1.0001325161617878
 Median: (1.176878520674877, 1.1769559924400335)
 Maximum: 4.911287035122236
 Mean: 1.3149000938066973
 Std Deviation: 0.4064166770845786
Post-processor distribution of 10000 samples using floor method:
 1: 93.96%
 2: 4.89%
 3: 0.82%
 4: 0.17%
 5: 0.07%
 6: 0.06%
 7: 0.01%
 8: 0.01%
 9: 0.01%

Output Analysis: paretovariate(4.0)
Typical Timing: 63 ± 14 ns
Statistics of 1024 samples:
 Minimum: 1.0004038871186138
 Median: (1.1827456786010337, 1.183140276182225)
 Maximum: 9.488170338199875
 Mean: 1.3345050922058397
 Std Deviation: 0.5263642496529202
Post-processor distribution of 10000 samples using floor method:
 1: 94.21%
 2: 4.78%
 3: 0.72%
 4: 0.16%
 5: 0.05%
 6: 0.03%
 7: 0.02%
 8: 0.01%
 9: 0.01%
 16: 0.01%

Base Case
Output Analysis: Random.gauss(1.0, 1.0)
Typical Timing: 563 ± 15 ns
Statistics of 1024 samples:
 Minimum: -2.503915673635692
 Median: (1.0080529635242343, 1.0092780304888354)
 Maximum: 4.049381362140805
 Mean: 0.9965379168150625
 Std Deviation: 1.0091403423005885
Post-processor distribution of 10000 samples using round method:
 -3: 0.07%
 -2: 0.66%
 -1: 6.1%
 0: 23.88%
 1: 38.7%
 2: 23.9%
 3: 6.09%
 4: 0.57%
 5: 0.03%

Output Analysis: gauss(1.0, 1.0)
Typical Timing: 94 ± 10 ns
Statistics of 1024 samples:
 Minimum: -1.9967225185088089
 Median: (0.98269138580719, 0.9872502031984696)
 Maximum: 4.174462164897991
 Mean: 1.0085787687407044
 Std Deviation: 0.9965164213701073
Post-processor distribution of 10000 samples using round method:
 -3: 0.01%
 -2: 0.61%
 -1: 6.06%
 0: 23.34%
 1: 39.2%
 2: 24.24%
 3: 6.07%
 4: 0.45%
 5: 0.02%

Base Case
Output Analysis: Random.normalvariate(0.0, 2.8)
Typical Timing: 625 ± 40 ns
Statistics of 1024 samples:
 Minimum: -8.906577454860782
 Median: (-0.010381891768346337, -0.0038892825878734374)
 Maximum: 8.94860731193175
 Mean: 0.07729613060070557
 Std Deviation: 2.6932152638932605
Post-processor distribution of 10000 samples using round method:
 -10: 0.01%
 -9: 0.05%
 -8: 0.25%
 -7: 0.62%
 -6: 1.49%
 -5: 2.86%
 -4: 4.94%
 -3: 7.96%
 -2: 11.04%
 -1: 13.58%
 0: 14.24%
 1: 13.37%
 2: 10.98%
 3: 7.79%
 4: 5.05%
 5: 3.15%
 6: 1.52%
 7: 0.71%
 8: 0.24%
 9: 0.12%
 10: 0.01%
 11: 0.02%

Output Analysis: normalvariate(0.0, 2.8)
Typical Timing: 94 ± 10 ns
Statistics of 1024 samples:
 Minimum: -8.833389985313884
 Median: (-0.14341546794708565, -0.12622529914275418)
 Maximum: 8.742524363852898
 Mean: -0.14459231833830563
 Std Deviation: 2.8262925940556403
Post-processor distribution of 10000 samples using round method:
 -10: 0.01%
 -9: 0.11%
 -8: 0.29%
 -7: 0.48%
 -6: 1.48%
 -5: 3.22%
 -4: 5.23%
 -3: 8.59%
 -2: 10.91%
 -1: 12.93%
 0: 14.85%
 1: 12.89%
 2: 11.01%
 3: 7.74%
 4: 5.03%
 5: 2.82%
 6: 1.4%
 7: 0.65%
 8: 0.28%
 9: 0.06%
 10: 0.02%

Base Case
Output Analysis: Random.lognormvariate(0.0, 0.5)
Typical Timing: 844 ± 37 ns
Statistics of 1024 samples:
 Minimum: 0.2056812295676593
 Median: (1.004859740398247, 1.0096460966362477)
 Maximum: 4.355213515829515
 Mean: 1.1377874926353742
 Std Deviation: 0.593950447830199
Post-processor distribution of 10000 samples using round method:
 0: 8.5%
 1: 70.88%
 2: 17.47%
 3: 2.77%
 4: 0.34%
 5: 0.04%

Output Analysis: lognormvariate(0.0, 0.5)
Typical Timing: 94 ± 6 ns
Statistics of 1024 samples:
 Minimum: 0.24478773122136593
 Median: (1.0330760328951165, 1.0340832270824878)
 Maximum: 5.924826402229223
 Mean: 1.1427240587631686
 Std Deviation: 0.589718988190048
Post-processor distribution of 10000 samples using round method:
 0: 8.17%
 1: 71.14%
 2: 17.19%
 3: 2.92%
 4: 0.43%
 5: 0.1%
 6: 0.04%
 7: 0.01%

Base Case
Output Analysis: Random.vonmisesvariate(0, 0)
Typical Timing: 250 ± 15 ns
Statistics of 1024 samples:
 Minimum: 0.01612427356793919
 Median: (2.9221686469285433, 2.9273203169855213)
 Maximum: 6.28159341776405
 Mean: 3.0910667869353725
 Std Deviation: 1.8384529300118235
Post-processor distribution of 10000 samples using floor method:
 0: 16.89%
 1: 15.54%
 2: 15.85%
 3: 15.61%
 4: 15.79%
 5: 15.56%
 6: 4.76%

Output Analysis: vonmisesvariate(0, 0)
Typical Timing: 63 ± 16 ns
Statistics of 1024 samples:
 Minimum: 0.007722400861515746
 Median: (3.147232212365276, 3.1557306106500884)
 Maximum: 6.282989384787309
 Mean: 3.129202757718238
 Std Deviation: 1.7898395529630284
Post-processor distribution of 10000 samples using floor method:
 0: 15.91%
 1: 15.94%
 2: 15.97%
 3: 15.37%
 4: 16.11%
 5: 15.89%
 6: 4.81%

Base Case
Output Analysis: Random.triangular(0.0, 10.0, 0.0)
Typical Timing: 469 ± 16 ns
Statistics of 1024 samples:
 Minimum: 0.008545720367314047
 Median: (2.8224616681257295, 2.826620815826196)
 Maximum: 9.744857933752824
 Mean: 3.36566934409453
 Std Deviation: 2.3902385626725846
Post-processor distribution of 10000 samples using floor method:
 0: 19.31%
 1: 16.96%
 2: 14.88%
 3: 12.57%
 4: 10.82%
 5: 8.92%
 6: 7.41%
 7: 5.22%
 8: 2.89%
 9: 1.02%

Output Analysis: triangular(0.0, 10.0, 0.0)
Typical Timing: 32 ± 12 ns
Statistics of 1024 samples:
 Minimum: 0.0024009705031122763
 Median: (3.0410426541210267, 3.0439577369566537)
 Maximum: 9.912147283441625
 Mean: 3.390622863550347
 Std Deviation: 2.3156942312721682
Post-processor distribution of 10000 samples using floor method:
 0: 19.2%
 1: 16.87%
 2: 15.28%
 3: 13.08%
 4: 10.84%
 5: 8.84%
 6: 6.8%
 7: 4.86%
 8: 3.18%
 9: 1.05%

Base Case
Output Analysis: Random.choice([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
Typical Timing: 719 ± 21 ns
Statistics of 1024 samples:
 Minimum: 0
 Median: 4
 Maximum: 9
 Mean: 4.369140625
 Std Deviation: 2.909932571323276
Distribution of 10000 samples:
 0: 9.65%
 1: 10.23%
 2: 10.35%
 3: 9.9%
 4: 10.08%
 5: 9.88%
 6: 9.77%
 7: 10.28%
 8: 9.9%
 9: 9.96%

Output Analysis: choice([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
Typical Timing: 63 ± 14 ns
Statistics of 1024 samples:
 Minimum: 0
 Median: 4
 Maximum: 9
 Mean: 4.451171875
 Std Deviation: 2.8442046500985523
Distribution of 10000 samples:
 0: 10.02%
 1: 10.04%
 2: 10.39%
 3: 9.63%
 4: 9.98%
 5: 10.38%
 6: 9.95%
 7: 9.57%
 8: 9.95%
 9: 10.09%

Base Case
Output Analysis: Random.choices([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [10, 9, 8, 7, 6, 5, 4, 3, 2, 1], k=1)
Typical Timing: 2282 ± 21 ns
Statistics of 1024 samples:
 Minimum: 0
 Median: 3
 Maximum: 9
 Mean: 2.966796875
 Std Deviation: 2.4392664101121606
Distribution of 10000 samples:
 0: 18.46%
 1: 16.37%
 2: 14.52%
 3: 12.77%
 4: 11.16%
 5: 8.94%
 6: 7.07%
 7: 5.2%
 8: 3.68%
 9: 1.83%

Output Analysis: choices([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [10, 9, 8, 7, 6, 5, 4, 3, 2, 1], k=1)
Typical Timing: 1157 ± 21 ns
Statistics of 1024 samples:
 Minimum: 0
 Median: 3
 Maximum: 9
 Mean: 3.0048828125
 Std Deviation: 2.478248598362546
Distribution of 10000 samples:
 0: 18.44%
 1: 15.63%
 2: 15.32%
 3: 12.77%
 4: 11.2%
 5: 8.89%
 6: 6.91%
 7: 5.36%
 8: 3.79%
 9: 1.69%

Base Case
Output Analysis: Random.choices([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], cum_weights=[10, 19, 27, 34, 40, 45, 49, 52, 54, 55], k=1)
Typical Timing: 1750 ± 18 ns
Statistics of 1024 samples:
 Minimum: 0
 Median: 3
 Maximum: 9
 Mean: 2.9775390625
 Std Deviation: 2.447989459411445
Distribution of 10000 samples:
 0: 17.7%
 1: 16.14%
 2: 14.64%
 3: 12.86%
 4: 11.12%
 5: 8.91%
 6: 7.73%
 7: 5.33%
 8: 3.74%
 9: 1.83%

Output Analysis: choices([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], cum_weights=[10, 19, 27, 34, 40, 45, 49, 52, 54, 55], k=1)
Typical Timing: 719 ± 16 ns
Statistics of 1024 samples:
 Minimum: 0
 Median: 2
 Maximum: 9
 Mean: 2.900390625
 Std Deviation: 2.4530468304628417
Distribution of 10000 samples:
 0: 18.39%
 1: 16.71%
 2: 13.84%
 3: 12.72%
 4: 11.04%
 5: 8.75%
 6: 7.54%
 7: 5.66%
 8: 3.51%
 9: 1.84%

Base Case
Timer only: random.shuffle(some_list) of size 10:
Typical Timing: 6625 ± 176 ns

Timer only: shuffle(some_list) of size 10:
Typical Timing: 500 ± 58 ns

Base Case
Output Analysis: Random.sample([0, 8, 4, 7, 3, 2, 1, 6, 5, 9], k=3)
Typical Timing: 4094 ± 51 ns
Statistics of 1024 samples:
 Minimum: 0
 Median: 5
 Maximum: 9
 Mean: 4.548828125
 Std Deviation: 2.8708871033643817
Distribution of 10000 samples:
 0: 10.2%
 1: 10.4%
 2: 10.0%
 3: 10.67%
 4: 9.39%
 5: 9.29%
 6: 10.43%
 7: 9.75%
 8: 9.7%
 9: 10.17%

Output Analysis: sample([0, 8, 4, 7, 3, 2, 1, 6, 5, 9], k=3)
Typical Timing: 782 ± 15 ns
Statistics of 1024 samples:
 Minimum: 0
 Median: 5
 Maximum: 9
 Mean: 4.5751953125
 Std Deviation: 2.8463778771430275
Distribution of 10000 samples:
 0: 9.53%
 1: 9.73%
 2: 9.55%
 3: 9.96%
 4: 10.62%
 5: 10.39%
 6: 9.69%
 7: 10.01%
 8: 10.46%
 9: 10.06%


Total Test Time: 1.521 sec