RNG 1.6.0

Python3 API for the C++ Random Library

Random Number Generator Engine for Python3

• Compiled Python3 API for the C++ Random Library
• Designed for python developers familiar with C++ Random.h
• Warning: RNG is not suitable for cryptography or secure hashing

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 RNG
$ python3
>>> import RNG ...

Installation may require the following:

• Python 3.6 or later with dev tools (setuptools, pip, etc.)
• Cython: pip install Cython
• Modern C++17 compiler and standard library for your platform.

---

RNG Specifications

Random Boolean

• RNG.bernoulli_variate(ratio_of_truth: float) -> bool
  • Produces a Bernoulli distribution of boolean values.
  • @param ratio_of_truth :: the probability of True. Expected input range: [0.0, 1.0], clamped.
  • @return :: True or False

Random Integer

• RNG.uniform_int_variate(left_limit: int, right_limit: int) -> int
  • Flat uniform distribution.
  • 20x faster than random.randint()
  • @param left_limit :: input A.
  • @param right_limit :: input B.
  • @return :: random integer in the inclusive range [A, B] or [B, A] if B < A
• RNG.binomial_variate(number_of_trials: int, probability: float) -> int
  • Based on the idea of flipping a coin and counting how many heads come up after some number of flips.
  • @param number_of_trials :: how many times to flip a coin.
  • @param probability :: how likely heads will be flipped. 0.5 is a fair coin. 1.0 is a double headed coin.
  • @return :: count of how many heads came up.
• RNG.negative_binomial_variate(trial_successes: int, probability: float) -> int
  • Based on the idea of flipping a coin as long as it takes to succeed.
  • @param trial_successes :: the required number of heads flipped to succeed.
  • @param probability :: how likely heads will be flipped. 0.50 is a fair coin.
  • @return :: the count of how many tails came up before the required number of heads.
• RNG.geometric_variate(probability: float) -> int
  • Same as random_negative_binomial(1, probability).
• RNG.poisson_variate(mean: float) -> int
  • @param mean :: sets the average output of the function.
  • @return :: random integer, poisson distribution centered on the mean.

Random Floating Point

• RNG.generate_canonical() -> float
  • Evenly distributes floats of maximum precision.
  • @return :: random float in range (0.0, 1.0)
• RNG.uniform_real_variate(left_limit: float, right_limit: float) -> float
  • Flat uniform distribution of floats.
  • @return :: random Float between left_limit and right_limit.
• RNG.normal_variate(mean: float, std_dev: float) -> float
  • @param mean :: sets the average output of the function.
  • @param std_dev :: standard deviation. Specifies spread of data from the mean.
• RNG.lognormal_variate(log_mean: float, log_deviation: float) -> float
  • @param log_mean :: sets the log of the mean of the function.
  • @param log_deviation :: log of the standard deviation. Specifies spread of data from the mean.
• RNG.exponential_variate(lambda_rate: float) -> float
  • Produces random non-negative floating-point values, distributed according to probability density function.
  • @param lambda_rate :: λ constant rate of a random event per unit of time/distance.
  • @return :: The time/distance until the next random event. For example, this distribution describes the time between the clicks of a Geiger counter or the distance between point mutations in a DNA strand.
• RNG.gamma_variate(shape: float, scale: float) -> float
  • Generalization of the exponential distribution.
  • Produces random positive floating-point values, distributed according to probability density function.
  • @param shape :: α the number of independent exponentially distributed random variables.
  • @param scale :: β the scale factor or the mean of each of the distributed random variables.
  • @return :: the sum of α independent exponentially distributed random variables, each of which has a mean of β.
• RNG.weibull_variate(shape: float, scale: float) -> float
  • Generalization of the exponential distribution.
  • Similar to the gamma distribution but uses a closed form distribution function.
  • Popular in reliability and survival analysis.
• RNG.extreme_value_variate(location: float, scale: float) -> float
  • Based on Extreme Value Theory.
  • Used for statistical models of the magnitude of earthquakes and volcanoes.
• RNG.chi_squared_variate(degrees_of_freedom: float) -> float
  • Used with the Chi Squared Test and Null Hypotheses to test if sample data fits an expected distribution.
• RNG.cauchy_variate(location: float, scale: float) -> float
  • @param location :: It specifies the location of the peak. The default value is 0.0.
  • @param scale :: It represents the half-width at half-maximum. The default value is 1.0.
  • @return :: Continuous Distribution.
• RNG.fisher_f_variate(degrees_of_freedom_1: float, degrees_of_freedom_2: float) -> float
  • F distributions often arise when comparing ratios of variances.
• RNG.student_t_variate(degrees_of_freedom: float) -> float
  • T distribution. Same as a normal distribution except it uses the sample standard deviation rather than the population standard deviation.
  • As degrees_of_freedom goes to infinity it converges with the normal distribution.

Development Log

RNG 1.6.0

• RNG is now compatible with python notebooks.

RNG 1.5.5

• Storm Update

RNG 1.5.4

• Storm 3.2 Update

RNG 1.5.3

• Fixed Typos

RNG 1.5.2

• Compiler Config Update

RNG 1.5.1

• A number of testing routines have been extracted into a new module: MonkeyScope.
  • distribution
  • timer
  • distribution_timer

RNG 1.5.0, internal

• Further API Refinements, new naming convention for variate generators: <algorithm name>_variate

RNG 1.4.2

• Install script update
• Test tweaks for noise reduction in timing tests.

RNG 1.4.1

• Test Patch for new API
• Documentation Updates

RNG 1.4.0

• API Refactoring

RNG 1.3.4

• Storm Update 3.1.1

RNG 1.3.3

• Installer script update

RNG 1.3.2

• Minor Bug Fix

RNG 1.3.1

• Test Update

RNG 1.3.1

• Fixed Typos

RNG 1.3.0

• Storm Update

RNG 1.2.5

• Low level clean up

RNG 1.2.4

• Minor Typos Fixed

RNG 1.2.3

• Documentation Update
• Test Update
• Bug Fixes

RNG 1.0.0 - 1.2.2, internal

• API Changes:
  • randint changed to random_int
  • randbelow changed to random_below
  • random changed to generate_canonical
  • uniform changed to random_float

RNG 0.2.3

• Bug Fixes

RNG 0.2.2

• discrete() removed.

RNG 0.2.1

• minor typos
• discrete() depreciated.

RNG 0.2.0

• Major Rebuild.

RNG 0.1.22

• The RNG Storm Engine is now the default standard.
• Experimental Vortex Engine added for testing.

RNG 0.1.21 beta

• Small update to the testing suite.

RNG 0.1.20 beta

• Changed default inputs for random_int and random_below to sane values.
  • random_int(left_limit=1, right_limit=20) down from -2**63, 2**63 - 1
  • random_below(upper_bound=10) down from 2**63 - 1

RNG 0.1.19 beta

• Broke some fixed typos, for a change of pace.

RNG 0.1.18 beta

• Fixed some typos.

RNG 0.1.17 beta

• Major Refactoring.
• New primary engine: Hurricane.
• Experimental engine Typhoon added: random_below() only.

RNG 0.1.16 beta

• Internal Engine Performance Tuning.

RNG 0.1.15 beta

• Engine Testing.

RNG 0.1.14 beta

• Fixed a few typos.

RNG 0.1.13 beta

• Fixed a few typos.

RNG 0.1.12 beta

• Major Test Suite Upgrade.
• Major Bug Fixes.
  • Removed several 'foot-guns' in prep for fuzz testing in future releases.

RNG 0.1.11 beta

• Fixed small bug in the install script.

RNG 0.1.10 beta

• Fixed some typos.

RNG 0.1.9 beta

• Fixed some typos.

RNG 0.1.8 beta

• Fixed some typos.
• More documentation added.

RNG 0.1.7 beta

• The random_floating_point function renamed to random_float.
• The function c_rand() has been removed as well as all the cruft it required.
• Major Documentation Upgrade.
• Fixed an issue where keyword arguments would fail to propagate. Both, positional args and kwargs now work as intended.
• Added this Dev Log.

RNG 0.0.6 alpha

• Minor ABI changes.

RNG 0.0.5 alpha

• Tests redesigned slightly for Float functions.

RNG 0.0.4 alpha

• Random Float Functions Implemented.

RNG 0.0.3 alpha

• Random Integer Functions Implemented.

RNG 0.0.2 alpha

• Random Bool Function Implemented.

RNG 0.0.1 pre-alpha

• Planning & Design.

MonkeyScope: Distribution and Performance Test Suite

MonkeyTimer: RNG Storm Engine
=========================================================================

Boolean Variate Distributions

Output Analysis: bernoulli_variate(0.0)
Typical Timing: 39 ± 8 ns
Statistics of 1000 samples:
 Minimum: False
 Median: False
 Maximum: False
 Mean: 0.0
 Std Deviation: 0.0
Distribution of 10000 samples:
 False: 100.0%

Output Analysis: bernoulli_variate(0.3333333333333333)
Typical Timing: 41 ± 7 ns
Statistics of 1000 samples:
 Minimum: False
 Median: False
 Maximum: True
 Mean: 0.32
 Std Deviation: 0.46647615158762396
Distribution of 10000 samples:
 False: 66.48%
 True: 33.52%

Output Analysis: bernoulli_variate(0.5)
Typical Timing: 45 ± 11 ns
Statistics of 1000 samples:
 Minimum: False
 Median: False
 Maximum: True
 Mean: 0.485
 Std Deviation: 0.49977494935220584
Distribution of 10000 samples:
 False: 50.34%
 True: 49.66%

Output Analysis: bernoulli_variate(0.6666666666666666)
Typical Timing: 36 ± 2 ns
Statistics of 1000 samples:
 Minimum: False
 Median: True
 Maximum: True
 Mean: 0.691
 Std Deviation: 0.4620811617021408
Distribution of 10000 samples:
 False: 32.61%
 True: 67.39%

Output Analysis: bernoulli_variate(1.0)
Typical Timing: 42 ± 11 ns
Statistics of 1000 samples:
 Minimum: True
 Median: True
 Maximum: True
 Mean: 1.0
 Std Deviation: 0.0
Distribution of 10000 samples:
 True: 100.0%


Integer Variate Distributions

Base Case
Output Analysis: Random.randint(1, 6)
Typical Timing: 1116 ± 73 ns
Statistics of 1000 samples:
 Minimum: 1
 Median: 4
 Maximum: 6
 Mean: 3.571
 Std Deviation: 1.725386623339824
Distribution of 10000 samples:
 1: 16.8%
 2: 16.2%
 3: 16.55%
 4: 16.18%
 5: 16.78%
 6: 17.49%

Output Analysis: uniform_int_variate(1, 6)
Typical Timing: 63 ± 13 ns
Statistics of 1000 samples:
 Minimum: 1
 Median: 3
 Maximum: 6
 Mean: 3.458
 Std Deviation: 1.7228569296375136
Distribution of 10000 samples:
 1: 16.06%
 2: 17.17%
 3: 16.61%
 4: 16.46%
 5: 17.18%
 6: 16.52%

Output Analysis: binomial_variate(4, 0.5)
Typical Timing: 135 ± 10 ns
Statistics of 1000 samples:
 Minimum: 0
 Median: 2
 Maximum: 4
 Mean: 2.028
 Std Deviation: 0.9955983125739014
Distribution of 10000 samples:
 0: 6.26%
 1: 25.15%
 2: 37.41%
 3: 25.14%
 4: 6.04%

Output Analysis: negative_binomial_variate(5, 0.75)
Typical Timing: 122 ± 8 ns
Statistics of 1000 samples:
 Minimum: 0
 Median: 1
 Maximum: 10
 Mean: 1.654
 Std Deviation: 1.5113847954773132
Distribution of 10000 samples:
 0: 23.17%
 1: 30.26%
 2: 22.73%
 3: 12.68%
 4: 6.12%
 5: 2.81%
 6: 1.27%
 7: 0.55%
 8: 0.31%
 9: 0.06%
 10: 0.02%
 11: 0.01%
 13: 0.01%

Output Analysis: geometric_variate(0.75)
Typical Timing: 53 ± 7 ns
Statistics of 1000 samples:
 Minimum: 0
 Median: 0
 Maximum: 5
 Mean: 0.347
 Std Deviation: 0.7032716402642722
Distribution of 10000 samples:
 0: 74.83%
 1: 18.92%
 2: 4.82%
 3: 1.09%
 4: 0.26%
 5: 0.06%
 6: 0.02%

Output Analysis: poisson_variate(4.5)
Typical Timing: 111 ± 2 ns
Statistics of 1000 samples:
 Minimum: 0
 Median: 4
 Maximum: 14
 Mean: 4.409
 Std Deviation: 2.20447703548937
Distribution of 10000 samples:
 0: 1.27%
 1: 5.08%
 2: 11.05%
 3: 17.03%
 4: 19.39%
 5: 16.97%
 6: 11.96%
 7: 8.05%
 8: 5.24%
 9: 2.28%
 10: 0.94%
 11: 0.48%
 12: 0.17%
 13: 0.08%
 14: 0.01%


Floating Point Variate Distributions

Base Case
Output Analysis: Random.random()
Typical Timing: 32 ± 2 ns
Statistics of 1000 samples:
 Minimum: 2.9811826205872194e-05
 Median: (0.4958182342666849, 0.4975786161860226)
 Maximum: 0.9997897111680522
 Mean: 0.4993473270436928
 Std Deviation: 0.28280868555450994
Post-processor distribution of 10000 samples using round method:
 0: 49.52%
 1: 50.48%

Output Analysis: generate_canonical()
Typical Timing: 47 ± 12 ns
Statistics of 1000 samples:
 Minimum: 2.493306491360936e-05
 Median: (0.5242533662658344, 0.5255452089458466)
 Maximum: 0.9998632428246992
 Mean: 0.5111776431861897
 Std Deviation: 0.28804110930526283
Post-processor distribution of 10000 samples using round method:
 0: 49.61%
 1: 50.39%

Base Case
Output Analysis: Random.uniform(0.0, 10.0)
Typical Timing: 248 ± 26 ns
Statistics of 1000 samples:
 Minimum: 0.012572446937733073
 Median: (4.955428940312675, 4.965659425115318)
 Maximum: 9.996267398690348
 Mean: 4.902269347425807
 Std Deviation: 2.827863977289873
Post-processor distribution of 10000 samples using floor method:
 0: 10.53%
 1: 10.1%
 2: 10.26%
 3: 9.91%
 4: 9.75%
 5: 10.12%
 6: 10.1%
 7: 9.84%
 8: 9.59%
 9: 9.8%

Output Analysis: uniform_real_variate(0.0, 10.0)
Typical Timing: 36 ± 2 ns
Statistics of 1000 samples:
 Minimum: 0.005108725383957314
 Median: (4.8532968375282515, 4.869074643059969)
 Maximum: 9.988949250587044
 Mean: 4.896104230213865
 Std Deviation: 2.8698892908402858
Post-processor distribution of 10000 samples using floor method:
 0: 9.96%
 1: 9.98%
 2: 10.02%
 3: 10.02%
 4: 9.98%
 5: 9.49%
 6: 9.71%
 7: 10.17%
 8: 10.4%
 9: 10.27%

Base Case
Output Analysis: Random.expovariate(1.0)
Typical Timing: 356 ± 29 ns
Statistics of 1000 samples:
 Minimum: 0.0026029260438604498
 Median: (0.661852884282279, 0.6627881018850212)
 Maximum: 8.570286686188908
 Mean: 0.9859568185635437
 Std Deviation: 1.0327476494707422
Post-processor distribution of 10000 samples using floor method:
 0: 63.45%
 1: 23.02%
 2: 8.65%
 3: 3.07%
 4: 1.0%
 5: 0.55%
 6: 0.21%
 7: 0.02%
 8: 0.02%
 12: 0.01%

Output Analysis: exponential_variate(1.0)
Typical Timing: 56 ± 7 ns
Statistics of 1000 samples:
 Minimum: 0.0002303021214451364
 Median: (0.6920941590909168, 0.6927329078352592)
 Maximum: 6.172623674118485
 Mean: 0.9616925550188851
 Std Deviation: 0.9320055200315461
Post-processor distribution of 10000 samples using floor method:
 0: 62.69%
 1: 23.32%
 2: 9.24%
 3: 3.19%
 4: 1.08%
 5: 0.31%
 6: 0.08%
 7: 0.06%
 8: 0.02%
 9: 0.01%

Base Case
Output Analysis: Random.gammavariate(1.0, 1.0)
Typical Timing: 492 ± 39 ns
Statistics of 1000 samples:
 Minimum: 0.0025607061316983227
 Median: (0.6992743974445291, 0.6997098084603889)
 Maximum: 6.390168402163173
 Mean: 1.0371859365208083
 Std Deviation: 1.0212780993102406
Post-processor distribution of 10000 samples using floor method:
 0: 63.09%
 1: 22.99%
 2: 8.79%
 3: 3.26%
 4: 1.13%
 5: 0.47%
 6: 0.18%
 7: 0.06%
 8: 0.03%

Output Analysis: gamma_variate(1.0, 1.0)
Typical Timing: 51 ± 1 ns
Statistics of 1000 samples:
 Minimum: 0.0007513907331859125
 Median: (0.6763044436875879, 0.67739013421492)
 Maximum: 8.002665918539234
 Mean: 0.9954605290602395
 Std Deviation: 0.9959437114628509
Post-processor distribution of 10000 samples using floor method:
 0: 63.67%
 1: 23.01%
 2: 8.63%
 3: 2.75%
 4: 1.24%
 5: 0.45%
 6: 0.12%
 7: 0.1%
 8: 0.03%

Base Case
Output Analysis: Random.weibullvariate(1.0, 1.0)
Typical Timing: 439 ± 33 ns
Statistics of 1000 samples:
 Minimum: 0.0006093908238637013
 Median: (0.7146215406718083, 0.7148095477634122)
 Maximum: 6.765843918525174
 Mean: 0.9895366762586641
 Std Deviation: 0.970311432808752
Post-processor distribution of 10000 samples using floor method:
 0: 62.99%
 1: 22.93%
 2: 8.55%
 3: 3.53%
 4: 1.36%
 5: 0.4%
 6: 0.16%
 7: 0.04%
 8: 0.03%
 9: 0.01%

Output Analysis: weibull_variate(1.0, 1.0)
Typical Timing: 97 ± 9 ns
Statistics of 1000 samples:
 Minimum: 1.3934649531694198e-05
 Median: (0.700348138942181, 0.700760193498743)
 Maximum: 7.05582282547319
 Mean: 0.9839996077544101
 Std Deviation: 0.9835331085877382
Post-processor distribution of 10000 samples using floor method:
 0: 63.24%
 1: 22.71%
 2: 8.91%
 3: 3.11%
 4: 1.31%
 5: 0.4%
 6: 0.19%
 7: 0.1%
 8: 0.03%

Output Analysis: extreme_value_variate(0.0, 1.0)
Typical Timing: 78 ± 8 ns
Statistics of 1000 samples:
 Minimum: -1.9647256270034987
 Median: (0.27933677297022186, 0.28140021530856857)
 Maximum: 7.16781915812163
 Mean: 0.5087474976195993
 Std Deviation: 1.3083985137743868
Post-processor distribution of 10000 samples using round method:
 -2: 1.09%
 -1: 18.43%
 0: 35.02%
 1: 25.45%
 2: 12.17%
 3: 4.87%
 4: 1.85%
 5: 0.72%
 6: 0.25%
 7: 0.11%
 8: 0.02%
 11: 0.01%
 12: 0.01%

Base Case
Output Analysis: Random.gauss(5.0, 2.0)
Typical Timing: 597 ± 12 ns
Statistics of 1000 samples:
 Minimum: -1.8599072870257993
 Median: (5.0212155800973255, 5.022133101582857)
 Maximum: 12.010947557767416
 Mean: 5.046939920689915
 Std Deviation: 2.014449420113154
Post-processor distribution of 10000 samples using round method:
 -3: 0.01%
 -2: 0.05%
 -1: 0.21%
 0: 0.9%
 1: 2.78%
 2: 6.31%
 3: 12.01%
 4: 16.96%
 5: 19.64%
 6: 17.69%
 7: 12.42%
 8: 6.36%
 9: 3.07%
 10: 1.22%
 11: 0.28%
 12: 0.09%

Output Analysis: normal_variate(5.0, 2.0)
Typical Timing: 90 ± 4 ns
Statistics of 1000 samples:
 Minimum: -1.6920149534264883
 Median: (5.046724502253657, 5.047181756289231)
 Maximum: 13.138823158374535
 Mean: 5.060301410808857
 Std Deviation: 2.0502935158912305
Post-processor distribution of 10000 samples using round method:
 -2: 0.1%
 -1: 0.27%
 0: 0.84%
 1: 2.89%
 2: 6.56%
 3: 11.49%
 4: 17.29%
 5: 20.19%
 6: 17.9%
 7: 11.83%
 8: 6.61%
 9: 2.58%
 10: 1.08%
 11: 0.28%
 12: 0.05%
 13: 0.04%

Base Case
Output Analysis: Random.lognormvariate(1.6, 0.25)
Typical Timing: 878 ± 31 ns
Statistics of 1000 samples:
 Minimum: 2.3186505332134217
 Median: (4.964194705282509, 4.965410727683739)
 Maximum: 12.372653855794
 Mean: 5.136088776648361
 Std Deviation: 1.3471592225591356
Post-processor distribution of 10000 samples using round method:
 2: 0.37%
 3: 7.63%
 4: 26.6%
 5: 31.25%
 6: 20.23%
 7: 9.09%
 8: 3.12%
 9: 1.28%
 10: 0.3%
 11: 0.1%
 12: 0.02%
 13: 0.01%

Output Analysis: lognormal_variate(1.6, 0.25)
Typical Timing: 118 ± 14 ns
Statistics of 1000 samples:
 Minimum: 2.1315182700271413
 Median: (4.989496814635092, 4.99426546149073)
 Maximum: 11.831655147085444
 Mean: 5.1363129686964895
 Std Deviation: 1.3286461687716329
Post-processor distribution of 10000 samples using round method:
 2: 0.3%
 3: 7.98%
 4: 26.22%
 5: 31.04%
 6: 20.52%
 7: 9.11%
 8: 3.22%
 9: 1.09%
 10: 0.36%
 11: 0.11%
 12: 0.04%
 13: 0.01%

Output Analysis: chi_squared_variate(1.0)
Typical Timing: 121 ± 11 ns
Statistics of 1000 samples:
 Minimum: 1.2102124763784665e-07
 Median: (0.4225117119864157, 0.427457107247602)
 Maximum: 10.837433223864931
 Mean: 0.9822637174812399
 Std Deviation: 1.4363844572581643
Post-processor distribution of 10000 samples using floor method:
 0: 68.28%
 1: 15.82%
 2: 7.41%
 3: 3.86%
 4: 1.93%
 5: 1.13%
 6: 0.62%
 7: 0.37%
 8: 0.27%
 9: 0.16%
 10: 0.06%
 11: 0.04%
 12: 0.03%
 14: 0.01%
 17: 0.01%

Output Analysis: cauchy_variate(0.0, 1.0)
Typical Timing: 87 ± 13 ns
Statistics of 1000 samples:
 Minimum: -700.923835541231
 Median: (0.05377537206444211, 0.05581762146388924)
 Maximum: 315.4456730340746
 Mean: -0.14178463325246776
 Std Deviation: 28.2496653866138
Post-processor distribution of 10000 samples using floor_mod_10 method:
 0: 26.28%
 1: 10.94%
 2: 5.34%
 3: 3.53%
 4: 3.2%
 5: 3.09%
 6: 3.87%
 7: 5.34%
 8: 11.63%
 9: 26.78%

Output Analysis: fisher_f_variate(8.0, 8.0)
Typical Timing: 207 ± 23 ns
Statistics of 1000 samples:
 Minimum: 0.07542299971622882
 Median: (0.979628476139982, 0.9801336498428839)
 Maximum: 17.548264949400025
 Mean: 1.332322278636915
 Std Deviation: 1.272955778035637
Post-processor distribution of 10000 samples using floor method:
 0: 50.14%
 1: 32.93%
 2: 10.16%
 3: 3.49%
 4: 1.4%
 5: 0.63%
 6: 0.39%
 7: 0.31%
 8: 0.18%
 9: 0.13%
 10: 0.07%
 11: 0.04%
 12: 0.01%
 14: 0.03%
 15: 0.01%
 16: 0.01%
 17: 0.02%
 18: 0.01%
 19: 0.02%
 20: 0.01%
 24: 0.01%

Output Analysis: student_t_variate(8.0)
Typical Timing: 164 ± 12 ns
Statistics of 1000 samples:
 Minimum: -6.9873545410474325
 Median: (0.010891867462137463, 0.011928439337986452)
 Maximum: 4.741453210266076
 Mean: 0.027883807950575115
 Std Deviation: 1.1362277199749644
Post-processor distribution of 10000 samples using round method:
 -7: 0.01%
 -6: 0.04%
 -5: 0.03%
 -4: 0.37%
 -3: 1.42%
 -2: 6.48%
 -1: 23.12%
 0: 37.39%
 1: 22.68%
 2: 6.55%
 3: 1.62%
 4: 0.19%
 5: 0.08%
 6: 0.01%
 8: 0.01%


=========================================================================
Total Test Time: 0.5454 seconds