派瓦克

快速、容错、直接替换 Python3 随机模块

建立在 RNG Storm Engine 之上，以提高稳定性和性能。Storm 是一个高质量的随机引擎，但完全不适合任何类型的密码学。Pyewacket 适用于游戏、数据科学、人工智能和实验性编程，而不是安全性。

快速安装$ pip install Pyewacket

安装可能需要以下内容：

• 带有开发工具（setuptools、pip 等）的 Python 3.6 或更高版本
• Cython：从 C/C++ 到 Python 的桥梁。
• 现代 C++17 编译器和标准库。

姐妹项目：

• Fortuna：制作自定义随机值生成器的工具集合。https://pypi.org/project/Fortuna/
• Pyewacket：Python3 随机模块的直接替换。https://pypi.org/project/Pyewacket/
• MonkeyScope：用于测试非确定性生成器的框架。https://pypi.org/project/MonkeyScope/

随机发生器

随机整数

• Pyewacket.randbelow(n: int) -> int
  • @param n :: Pyewacket 扩展了可接受的输入域以包含 n 的非正值。
  • @return :: 范围 (n, 0] 或 [0, n) 中的随机整数，取决于 n 的符号。

from Pyewacket import randbelow


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

• Pyewacket.randint(a: int, b: int) -> int
  • @param a, b :: 输入形成输出分布范围
  • @return :: [a, b] 或 [b, a] 范围内的随机整数
  • 包容双方
  • 删除了 a < b 的不对称要求
  • 当 a == b 这总是返回 a

from Pyewacket import randint


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

• Pyewacket.randrange(start: int, stop: int = 0, step: int = 1) -> int
  • @param start :: 这是分布范围的起点，只要 start <= stop 和 step >= 0
  • @param stop :: 可选，默认=0，声明点当且仅当 stop < start。
  • @param step :: 可选，默认=1，负步进将翻转开始和停止。
    • 步骤的符号控制输出的相位，换句话说：负范围向后计数。
  • @return :: 范围内的随机整数 (stop, start] 或 [start, stop)，增量为 |step|
  • 去掉了 start < stop 和 step > 0 的要求
  • 总是返回 start 如果 start == stop 或 step == 0

from Pyewacket import randrange


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
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

随机浮点

• Pyewacket.random() -> float
  • 在 [0.0, 1.0] 或 [0.0, 1.0) 范围内的随机浮点数取决于舍入，平台特定。
• Pyewacket.uniform(a: float, b: float) -> float
  • [a, b] 或 [a, b) 中的随机浮点数取决于舍入
• Pyewacket.expovariate(lambd: float) -> float
• Pyewacket.gammavariate(alpha, beta) -> float
• Pyewacket.weibullvariate(alpha, beta) -> float
• Pyewacket.betavariate(alpha, beta) -> float
• Pyewacket.paretovariate(alpha) -> float
• Pyewacket.gauss(mu: float, sigma: float) -> float
• Pyewacket.normalvariate(mu: float, sigma: float) -> float与 Pyewacket.gauss() 相同
• Pyewacket.lognormvariate(mu: float, sigma: float) -> float
• Pyewacket.vonmisesvariate(mu: float, kappa: float) -> float
• Pyewacket.triangular(low: float, high: float, mode: float = None)

随机序列值

• Pyewacket.choice(seq: List) -> Value
  • @param seq :: 任何零索引对象，如列表或元组。
  • @return :: 列表中的随机值，可以是可以放入列表中的任何对象类型。
• Pyewacket.choices(population, weights=None, *, cum_weights=None, k=1)
  • @param 人口 :: 数据值
  • @param weights :: 相对权重
  • @param cum_weights :: 累积权重
  • @param k :: 要收集的样本数
• Pyewacket.cumulative_weighted_choice(table, k=1)
  • 仅支持累积权重。如果需要，将相对权重转换为累积权重：cum_weights = tuple(itertools.accumulate(rel_weights))
  • @param table :: 加权值对的二维列表或元组。[(1, "a"), (10, "b"), (100, "c")...]
    • 该表可以构建为tuple(zip(cum_weights, population))权重总是第一位的。
  • @param k :: 要收集的样本数。如果 k > 1，则返回大小为 k 的列表，否则返回单个值 - 不是一个列表。
• Pyewacket.shuffle(array: list) -> None
  • 随机播放列表。
  • @param array :: 必须是一个可变列表。
  • 实现 Knuth B Shuffle 算法。对于每个测试用例，Knuth B 的速度是 Knuth A 或 Fisher-Yates 的两倍。这可能是由于向后走和向后旋转到列表背面的组合。使用这种组合，它永远无法修改它仍然需要遍历的数据。一路上都是新鲜的雪，缓存未命中的概率非常低。
• Pyewacket.sample(population: List, k: int) -> list
  • @param 人口:: 列表或元组。
  • @param k :: 要获取的唯一样本数。
  • @return :: size k 唯一随机样本列表。

硬件和软件播种

• seed(seed: int=0) -> None
  • 默认情况下启用硬件播种。此功能用于打开切换软件种子并设置或重置引擎种子。这会影响模块中的所有随机函数。
  • @param seed :: 任何小于 2**63 的非零正整数都可以启用软件播种。
  • 调用seed()orseed(0)将关闭软件播种并重新启用硬件播种。
  • 虽然您可以打开和关闭软件播种并随意重新播种引擎而不会出错，但此功能不适合在紧密循环中使用。一般规则：播种一次，或者更好，根本不播种。通常，软件播种用于调试产品，硬件播种用于产品发布。请不要为游戏的发布版本使用软件播种！

开发日志

派瓦特 1.3.9

• 安装程序更新

派瓦特 1.3.8

• 文档更新

Pyewacket 1.3.7

• 修正了更多的错别字

Pyewacket 1.3.6

• 固定错别字

Pyewacket 1.3.5

• 安装程序更新

Pyewacket 1.3.4

• 风暴 3.2.2 更新。

Pyewacket 1.3.3

• Pyewacket 现在与 python 笔记本兼容。

Pyewacket 1.3.2

• 风暴更新

Pyewacket 1.3.1

• 风暴更新

Pyewacket 1.3.0

• 主要 API 更新，几个实用程序已被移到他们自己的模块中：MonkeyScope。
  • 分发定时器
  • 分配
  • 计时器

Pyewacket 1.2.4

• Pyewacket.randrange()错误修复
• 测试更新

派瓦特 1.2.3

• 小错误修复

派瓦特 1.2.2

• 错字修复

派瓦特 1.2.1

• 测试更新

派瓦特 1.2.0

• 风暴更新

Pyewacket 1.1.2

• 低级清理

派瓦特 1.1.1

• 文档更新

Pyewacket 1.1.0

• 风暴引擎更新

Pyewacket 1.0.3

• 小错别字

派瓦特 1.0.2

• 增加的选择cumulative_weighted_choice

派瓦特 1.0.1

• 小错别字

派瓦特 1.0.0

• 风暴 2 重建。

Pyewacket 0.1.22

• 小错误修复。

Pyewacket 0.1.21

• 公开发布

Pyewacket 0.0.2b1

• 添加了软件播种。

Pyewacket v0.0.1b8

• 修复了测试中的一个小错误。

Pyewacket v0.0.1b7

• 发动机微调
• 修正了一些错别字。

Pyewacket v0.0.1b6

• 重新安排测试以更加一致并匹配文档。

Pyewacket v0.0.1b5

• 文档升级
• 轻微的性能调整

Pyewacket v0.0.1b4

• 公开测试版

Pyewacket v0.0.1b3

• 快速测试（）
• 扩展功能
  • 样本（）
  • 显式变量（）
  • 伽玛变量（）
  • 威布尔变量（）
  • 贝塔变量（）
  • 帕累托变量（）
  • 高斯（）
  • 正态变量（）
  • 对数规范变量（）
  • vonmisesvariate()
  • 三角形（）

Pyewacket v0.0.1b2

• 基本功能
  • 随机的（）
  • 制服（）
  • 兰德以下（）
  • 随机数（）
  • 随机范围（）
  • 选择（）
  • 选择（）
  • 洗牌（）

Pyewacket v0.0.1b1

• 初步设计与规划

分布和性能测试

MonkeyScope: Pyewacket

Base Case
Output Analysis: Random._randbelow(10)
Typical Timing: 581 ± 20 ns
Statistics of 1000 samples:
 Minimum: 0
 Median: 5
 Maximum: 9
 Mean: 4.557
 Std Deviation: 2.8430179387404504
Distribution of 10000 samples:
 0: 9.9%
 1: 10.19%
 2: 10.64%
 3: 10.21%
 4: 10.19%
 5: 10.02%
 6: 10.09%
 7: 9.63%
 8: 9.47%
 9: 9.66%

Output Analysis: randbelow(10)
Typical Timing: 67 ± 10 ns
Statistics of 1000 samples:
 Minimum: 0
 Median: 4
 Maximum: 9
 Mean: 4.425
 Std Deviation: 2.8692115641757754
Distribution of 10000 samples:
 0: 9.92%
 1: 9.48%
 2: 10.36%
 3: 10.7%
 4: 9.92%
 5: 9.85%
 6: 10.38%
 7: 9.96%
 8: 9.76%
 9: 9.67%

Base Case
Output Analysis: Random.randint(1, 10)
Typical Timing: 1148 ± 71 ns
Statistics of 1000 samples:
 Minimum: 1
 Median: 5
 Maximum: 10
 Mean: 5.394
 Std Deviation: 2.8500463154131372
Distribution of 10000 samples:
 1: 10.1%
 2: 10.43%
 3: 9.63%
 4: 9.85%
 5: 9.46%
 6: 9.83%
 7: 10.15%
 8: 10.7%
 9: 9.64%
 10: 10.21%

Output Analysis: randint(1, 10)
Typical Timing: 61 ± 8 ns
Statistics of 1000 samples:
 Minimum: 1
 Median: 6
 Maximum: 10
 Mean: 5.566
 Std Deviation: 2.871871167026822
Distribution of 10000 samples:
 1: 10.52%
 2: 9.61%
 3: 9.96%
 4: 10.1%
 5: 9.95%
 6: 9.38%
 7: 10.66%
 8: 9.84%
 9: 10.13%
 10: 9.85%

Base Case
Output Analysis: Random.randrange(0, 10, 2)
Typical Timing: 1248 ± 73 ns
Statistics of 1000 samples:
 Minimum: 0
 Median: 4
 Maximum: 8
 Mean: 3.946
 Std Deviation: 2.7873076615257237
Distribution of 10000 samples:
 0: 20.18%
 2: 19.76%
 4: 21.0%
 6: 19.9%
 8: 19.16%

Output Analysis: randrange(0, 10, 2)
Typical Timing: 98 ± 16 ns
Statistics of 1000 samples:
 Minimum: 0
 Median: 4
 Maximum: 8
 Mean: 3.834
 Std Deviation: 2.8072128526351547
Distribution of 10000 samples:
 0: 20.61%
 2: 20.39%
 4: 20.07%
 6: 19.58%
 8: 19.35%

Base Case
Output Analysis: Random.random()
Typical Timing: 37 ± 6 ns
Statistics of 1000 samples:
 Minimum: 0.0022025335119719713
 Median: (0.504236734486946, 0.5043377592978666)
 Maximum: 0.9988675528749947
 Mean: 0.4992614084502893
 Std Deviation: 0.29450740919885326
Post-processor distribution of 10000 samples using round method:
 0: 50.17%
 1: 49.83%

Output Analysis: random()
Typical Timing: 36 ± 1 ns
Statistics of 1000 samples:
 Minimum: 0.0005611174645538731
 Median: (0.5035689629010788, 0.5043732233487602)
 Maximum: 0.9997053348692302
 Mean: 0.5062137836044255
 Std Deviation: 0.2872867534109569
Post-processor distribution of 10000 samples using round method:
 0: 50.19%
 1: 49.81%

Base Case
Output Analysis: Random.uniform(0.0, 10.0)
Typical Timing: 239 ± 21 ns
Statistics of 1000 samples:
 Minimum: 0.00855387357446502
 Median: (4.8377420821319275, 4.839112429261609)
 Maximum: 9.944528377056002
 Mean: 4.985114491741573
 Std Deviation: 2.854828406573762
Post-processor distribution of 10000 samples using floor method:
 0: 9.97%
 1: 9.86%
 2: 9.47%
 3: 9.99%
 4: 10.33%
 5: 9.99%
 6: 9.88%
 7: 10.45%
 8: 10.33%
 9: 9.73%

Output Analysis: uniform(0.0, 10.0)
Typical Timing: 40 ± 6 ns
Statistics of 1000 samples:
 Minimum: 0.014370725680160675
 Median: (4.932233828685737, 4.934800131365183)
 Maximum: 9.991743209602872
 Mean: 4.944711192504797
 Std Deviation: 2.903933069340305
Post-processor distribution of 10000 samples using floor method:
 0: 9.97%
 1: 9.87%
 2: 10.1%
 3: 9.71%
 4: 10.15%
 5: 10.05%
 6: 9.36%
 7: 10.35%
 8: 10.29%
 9: 10.15%

Base Case
Output Analysis: Random.expovariate(1.0)
Typical Timing: 344 ± 20 ns
Statistics of 1000 samples:
 Minimum: 0.00013930738155526723
 Median: (0.6974151830509201, 0.6982605474669916)
 Maximum: 7.006299712833918
 Mean: 0.9851091283909009
 Std Deviation: 0.9482631726906081
Post-processor distribution of 10000 samples using floor method:
 0: 63.02%
 1: 23.19%
 2: 8.99%
 3: 3.01%
 4: 1.15%
 5: 0.38%
 6: 0.16%
 7: 0.04%
 8: 0.04%
 10: 0.01%
 13: 0.01%

Output Analysis: expovariate(1.0)
Typical Timing: 55 ± 6 ns
Statistics of 1000 samples:
 Minimum: 0.0005268217098112992
 Median: (0.7287325498157464, 0.729028105461747)
 Maximum: 6.423738021042586
 Mean: 1.010884902851076
 Std Deviation: 0.9798177662432959
Post-processor distribution of 10000 samples using floor method:
 0: 62.92%
 1: 23.47%
 2: 8.41%
 3: 3.2%
 4: 1.3%
 5: 0.35%
 6: 0.27%
 7: 0.05%
 8: 0.03%

Base Case
Output Analysis: Random.gammavariate(2.0, 1.0)
Typical Timing: 1216 ± 39 ns
Statistics of 1000 samples:
 Minimum: 0.022123265128863975
 Median: (1.6738025588508376, 1.6869422953529067)
 Maximum: 9.144862623568999
 Mean: 1.9946207548427557
 Std Deviation: 1.3831794343166977
Post-processor distribution of 10000 samples using round method:
 0: 9.04%
 1: 34.77%
 2: 27.07%
 3: 15.54%
 4: 7.6%
 5: 3.25%
 6: 1.53%
 7: 0.75%
 8: 0.21%
 9: 0.13%
 10: 0.06%
 11: 0.01%
 12: 0.02%
 13: 0.01%
 14: 0.01%

Output Analysis: gammavariate(2.0, 1.0)
Typical Timing: 114 ± 5 ns
Statistics of 1000 samples:
 Minimum: 0.05050761827178252
 Median: (1.7155254242728513, 1.7155403374497076)
 Maximum: 9.474394865820214
 Mean: 2.0432583626258274
 Std Deviation: 1.440053170380605
Post-processor distribution of 10000 samples using round method:
 0: 8.35%
 1: 36.19%
 2: 26.63%
 3: 15.04%
 4: 7.56%
 5: 3.61%
 6: 1.59%
 7: 0.58%
 8: 0.33%
 9: 0.06%
 10: 0.03%
 11: 0.03%

Base Case
Output Analysis: Random.weibullvariate(1.0, 1.0)
Typical Timing: 433 ± 31 ns
Statistics of 1000 samples:
 Minimum: 0.0013211102177539506
 Median: (0.689421374168411, 0.6902805115868248)
 Maximum: 6.763716954010422
 Mean: 1.0145977021774952
 Std Deviation: 1.0058606176825422
Post-processor distribution of 10000 samples using floor method:
 0: 63.62%
 1: 22.96%
 2: 8.29%
 3: 3.2%
 4: 1.32%
 5: 0.36%
 6: 0.18%
 7: 0.04%
 8: 0.01%
 10: 0.02%

Output Analysis: weibullvariate(1.0, 1.0)
Typical Timing: 103 ± 15 ns
Statistics of 1000 samples:
 Minimum: 0.00143486573238355
 Median: (0.6919630243174832, 0.6933880404695633)
 Maximum: 7.915315904014041
 Mean: 0.9999870976051519
 Std Deviation: 1.0199621642753662
Post-processor distribution of 10000 samples using floor method:
 0: 63.82%
 1: 22.46%
 2: 8.53%
 3: 3.25%
 4: 1.21%
 5: 0.47%
 6: 0.18%
 7: 0.06%
 8: 0.01%
 9: 0.01%

Base Case
Output Analysis: Random.betavariate(3.0, 3.0)
Typical Timing: 2558 ± 78 ns
Statistics of 1000 samples:
 Minimum: 0.039839528361731796
 Median: (0.5015726481908723, 0.5029699817553287)
 Maximum: 0.9435702178836589
 Mean: 0.5000214739643288
 Std Deviation: 0.18805632108618242
Post-processor distribution of 10000 samples using round method:
 0: 49.34%
 1: 50.66%

Output Analysis: betavariate(3.0, 3.0)
Typical Timing: 199 ± 16 ns
Statistics of 1000 samples:
 Minimum: 0.014386257468019166
 Median: (0.5041917967703041, 0.5043820043201677)
 Maximum: 0.9224068506980523
 Mean: 0.5036287644629798
 Std Deviation: 0.19311990407311008
Post-processor distribution of 10000 samples using round method:
 0: 50.03%
 1: 49.97%

Base Case
Output Analysis: Random.paretovariate(4.0)
Typical Timing: 299 ± 22 ns
Statistics of 1000 samples:
 Minimum: 1.0003778533014105
 Median: (1.165002857930227, 1.165698705069618)
 Maximum: 10.516678707241427
 Mean: 1.3111050026121693
 Std Deviation: 0.5187788907898297
Post-processor distribution of 10000 samples using floor method:
 1: 93.54%
 2: 5.2%
 3: 0.93%
 4: 0.19%
 5: 0.03%
 6: 0.04%
 7: 0.04%
 10: 0.01%
 11: 0.01%
 12: 0.01%

Output Analysis: paretovariate(4.0)
Typical Timing: 79 ± 6 ns
Statistics of 1000 samples:
 Minimum: 1.0000021203352474
 Median: (1.1950046733402977, 1.1960253759614277)
 Maximum: 4.928427639622488
 Mean: 1.3384992333095633
 Std Deviation: 0.44348583284188964
Post-processor distribution of 10000 samples using floor method:
 1: 93.57%
 2: 5.14%
 3: 0.83%
 4: 0.32%
 5: 0.08%
 6: 0.03%
 7: 0.01%
 8: 0.01%
 9: 0.01%

Base Case
Output Analysis: Random.gauss(1.0, 1.0)
Typical Timing: 597 ± 27 ns
Statistics of 1000 samples:
 Minimum: -1.9198822626936378
 Median: (1.005335898102709, 1.011229972843203)
 Maximum: 3.995102828970162
 Mean: 1.0036902758611341
 Std Deviation: 1.0049002779744916
Post-processor distribution of 10000 samples using round method:
 -3: 0.01%
 -2: 0.7%
 -1: 6.54%
 0: 24.32%
 1: 37.67%
 2: 24.28%
 3: 5.86%
 4: 0.57%
 5: 0.05%

Output Analysis: gauss(1.0, 1.0)
Typical Timing: 84 ± 2 ns
Statistics of 1000 samples:
 Minimum: -2.105377657621053
 Median: (0.9677613928765401, 0.9738364825460277)
 Maximum: 3.9619897840596185
 Mean: 0.9803515870690724
 Std Deviation: 0.9847928983953689
Post-processor distribution of 10000 samples using round method:
 -3: 0.03%
 -2: 0.58%
 -1: 6.02%
 0: 24.3%
 1: 38.52%
 2: 23.92%
 3: 5.93%
 4: 0.67%
 5: 0.03%

Base Case
Output Analysis: Random.normalvariate(0.0, 2.8)
Typical Timing: 686 ± 22 ns
Statistics of 1000 samples:
 Minimum: -9.36705533019951
 Median: (-0.08343328178059332, -0.07242218755420544)
 Maximum: 9.638137159093363
 Mean: 0.015326886488786868
 Std Deviation: 2.896301190156864
Post-processor distribution of 10000 samples using round method:
 -10: 0.03%
 -9: 0.12%
 -8: 0.24%
 -7: 0.71%
 -6: 1.38%
 -5: 3.12%
 -4: 4.93%
 -3: 8.03%
 -2: 11.44%
 -1: 13.25%
 0: 14.37%
 1: 13.02%
 2: 10.66%
 3: 8.17%
 4: 5.2%
 5: 3.02%
 6: 1.29%
 7: 0.69%
 8: 0.22%
 9: 0.07%
 10: 0.04%

Output Analysis: normalvariate(0.0, 2.8)
Typical Timing: 84 ± 2 ns
Statistics of 1000 samples:
 Minimum: -9.046455801727028
 Median: (0.004054759430993154, 0.021315402592363517)
 Maximum: 9.578970261780695
 Mean: -0.014712782228340401
 Std Deviation: 2.760000323856411
Post-processor distribution of 10000 samples using round method:
 -11: 0.01%
 -10: 0.04%
 -9: 0.05%
 -8: 0.16%
 -7: 0.69%
 -6: 1.61%
 -5: 3.04%
 -4: 5.24%
 -3: 7.95%
 -2: 10.99%
 -1: 13.18%
 0: 14.69%
 1: 13.53%
 2: 11.0%
 3: 7.66%
 4: 4.95%
 5: 2.59%
 6: 1.46%
 7: 0.73%
 8: 0.3%
 9: 0.09%
 10: 0.02%
 11: 0.01%
 13: 0.01%

Base Case
Output Analysis: Random.lognormvariate(0.0, 0.5)
Typical Timing: 878 ± 53 ns
Statistics of 1000 samples:
 Minimum: 0.2248693655862111
 Median: (1.0456443550688597, 1.0463295395067145)
 Maximum: 5.681692998787057
 Mean: 1.1774875785497383
 Std Deviation: 0.6355607662063447
Post-processor distribution of 10000 samples using round method:
 0: 8.24%
 1: 71.03%
 2: 17.45%
 3: 2.71%
 4: 0.43%
 5: 0.11%
 6: 0.02%
 9: 0.01%

Output Analysis: lognormvariate(0.0, 0.5)
Typical Timing: 109 ± 9 ns
Statistics of 1000 samples:
 Minimum: 0.21215863870079615
 Median: (0.9708663230257852, 0.971472722331232)
 Maximum: 4.11173040529319
 Mean: 1.0903966237459795
 Std Deviation: 0.5587946576471575
Post-processor distribution of 10000 samples using round method:
 0: 8.65%
 1: 70.43%
 2: 17.45%
 3: 2.91%
 4: 0.48%
 5: 0.05%
 6: 0.03%

Base Case
Output Analysis: Random.vonmisesvariate(0, 0)
Typical Timing: 270 ± 21 ns
Statistics of 1000 samples:
 Minimum: 0.004918010643852079
 Median: (3.212229751626989, 3.2201766390537983)
 Maximum: 6.257997091009342
 Mean: 3.140987802653663
 Std Deviation: 1.7890683319823657
Post-processor distribution of 10000 samples using floor method:
 0: 16.05%
 1: 16.0%
 2: 15.44%
 3: 16.56%
 4: 15.69%
 5: 15.67%
 6: 4.59%

Output Analysis: vonmisesvariate(0, 0)
Typical Timing: 70 ± 9 ns
Statistics of 1000 samples:
 Minimum: 0.00460539766627348
 Median: (3.084578088420162, 3.0866691165283298)
 Maximum: 6.278298447163166
 Mean: 3.115976931902511
 Std Deviation: 1.7888917760687573
Post-processor distribution of 10000 samples using floor method:
 0: 15.94%
 1: 15.94%
 2: 15.94%
 3: 15.77%
 4: 15.78%
 5: 15.93%
 6: 4.7%

Base Case
Output Analysis: Random.triangular(0.0, 10.0, 0.0)
Typical Timing: 495 ± 19 ns
Statistics of 1000 samples:
 Minimum: 0.003237961622717833
 Median: (2.756018739981477, 2.77041555411836)
 Maximum: 9.679493886664185
 Mean: 3.217204114204818
 Std Deviation: 2.2695608348990586
Post-processor distribution of 10000 samples using floor method:
 0: 19.81%
 1: 16.45%
 2: 15.12%
 3: 12.91%
 4: 11.14%
 5: 8.9%
 6: 6.77%
 7: 4.82%
 8: 3.08%
 9: 1.0%

Output Analysis: triangular(0.0, 10.0, 0.0)
Typical Timing: 43 ± 1 ns
Statistics of 1000 samples:
 Minimum: 0.0003452204741609677
 Median: (3.050051144263745, 3.0566898002269616)
 Maximum: 9.787833018174565
 Mean: 3.335322504335237
 Std Deviation: 2.303377888450858
Post-processor distribution of 10000 samples using floor method:
 0: 18.17%
 1: 16.9%
 2: 15.42%
 3: 12.71%
 4: 11.34%
 5: 9.08%
 6: 7.48%
 7: 4.89%
 8: 3.02%
 9: 0.99%

Base Case
Output Analysis: Random.choice([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
Typical Timing: 789 ± 39 ns
Statistics of 1000 samples:
 Minimum: 0
 Median: 5
 Maximum: 9
 Mean: 4.604
 Std Deviation: 2.8201390036663083
Distribution of 10000 samples:
 0: 10.18%
 1: 9.69%
 2: 9.88%
 3: 9.94%
 4: 10.21%
 5: 9.87%
 6: 9.82%
 7: 10.29%
 8: 9.56%
 9: 10.56%

Output Analysis: choice([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
Typical Timing: 75 ± 10 ns
Statistics of 1000 samples:
 Minimum: 0
 Median: 5
 Maximum: 9
 Mean: 4.531
 Std Deviation: 2.9125657074133113
Distribution of 10000 samples:
 0: 9.55%
 1: 10.29%
 2: 9.91%
 3: 9.94%
 4: 10.03%
 5: 10.1%
 6: 10.73%
 7: 10.19%
 8: 10.0%
 9: 9.26%

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: 2374 ± 70 ns
Statistics of 1000 samples:
 Minimum: 0
 Median: 3
 Maximum: 9
 Mean: 2.944
 Std Deviation: 2.4076677511650146
Distribution of 10000 samples:
 0: 17.9%
 1: 16.81%
 2: 14.91%
 3: 12.4%
 4: 10.98%
 5: 8.8%
 6: 7.55%
 7: 5.4%
 8: 3.54%
 9: 1.71%

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: 1147 ± 56 ns
Statistics of 1000 samples:
 Minimum: 0
 Median: 3
 Maximum: 9
 Mean: 3.016
 Std Deviation: 2.4754280437936385
Distribution of 10000 samples:
 0: 18.44%
 1: 16.19%
 2: 14.63%
 3: 12.99%
 4: 10.77%
 5: 8.88%
 6: 7.59%
 7: 5.21%
 8: 3.57%
 9: 1.73%

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: 1782 ± 52 ns
Statistics of 1000 samples:
 Minimum: 0
 Median: 2
 Maximum: 9
 Mean: 2.886
 Std Deviation: 2.4594723011247757
Distribution of 10000 samples:
 0: 17.65%
 1: 16.66%
 2: 14.8%
 3: 12.17%
 4: 11.21%
 5: 9.01%
 6: 7.36%
 7: 5.67%
 8: 3.66%
 9: 1.81%

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: 702 ± 20 ns
Statistics of 1000 samples:
 Minimum: 0
 Median: 3
 Maximum: 9
 Mean: 3.054
 Std Deviation: 2.409374192606869
Distribution of 10000 samples:
 0: 17.91%
 1: 15.49%
 2: 14.94%
 3: 12.73%
 4: 10.98%
 5: 9.59%
 6: 7.51%
 7: 5.46%
 8: 3.54%
 9: 1.85%

Base Case
Timer only: random.shuffle(some_list) of size 10:
Typical Timing: 8322 ± 1705 ns

Timer only: shuffle(some_list) of size 10:
Typical Timing: 794 ± 317 ns

Base Case
Output Analysis: Random.sample([5, 7, 8, 2, 6, 3, 4, 9, 1, 0], k=3)
Typical Timing: 4137 ± 161 ns
Statistics of 1000 samples:
 Minimum: 0
 Median: 4
 Maximum: 9
 Mean: 4.485
 Std Deviation: 2.9294666750109997
Distribution of 10000 samples:
 0: 10.03%
 1: 10.19%
 2: 9.98%
 3: 10.05%
 4: 9.92%
 5: 9.44%
 6: 10.59%
 7: 9.77%
 8: 10.26%
 9: 9.77%

Output Analysis: sample([5, 7, 8, 2, 6, 3, 4, 9, 1, 0], k=3)
Typical Timing: 848 ± 20 ns
Statistics of 1000 samples:
 Minimum: 0
 Median: 5
 Maximum: 9
 Mean: 4.568
 Std Deviation: 2.8631060057217583
Distribution of 10000 samples:
 0: 9.8%
 1: 10.03%
 2: 10.4%
 3: 9.73%
 4: 10.17%
 5: 10.1%
 6: 9.76%
 7: 10.08%
 8: 10.13%
 9: 9.8%


Total Test Time: 1.986 sec