Metadata-Version: 2.1
Name: RNG
Version: 1.7.2
Summary: Python3 API for the C++ Random Library
Home-page: UNKNOWN
Author: Robert Sharp
Author-email: webmaster@sharpdesigndigital.com
License: Free for non-commercial use
Description: # Random Number Generator & Engine for Python3
        - Compiled Python3 API for the C++ Random Library.
        - Designed for python developers familiar with C++ random library.
        - Warning: RNG is not suitable for cryptography or secure hashing.
        
        
        ### Quick Install `$ pip install RNG`
        
        
        ### Installation may require the following:
        - Python 3.6 or later with dev tools (setuptools, pip, etc.)
        - Cython: Bridge from C/C++ to Python.
        - Modern C++17 Compiler and Standard Library.
        
        
        ### 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 generators. https://pypi.org/project/MonkeyScope/
        
        
        ---
        
        ## 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.
            - @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.
        - `RNG.beta_variate(alpha, beta) -> float`
        - `RNG.pareto_variate(alpha) -> float`
        - `RNG.vonmises_variate(mu: float, kappa: float) -> float`
        - `RNG.triangular_variate(low: float, high: float, mode: float = None)`
        
        
        ## Development Log
        ##### RNG 1.7.2
        - Adds four new functions:
            - beta_variate
            - pareto_variate
            - vonmises_variate
            - triangular_variate
        
        ##### RNG 1.7.1
        - Fixes Major Bug in 1.7.0
        
        ##### RNG 1.7.0
        - Storm 3.3.3 update
        
        ##### RNG 1.6.7
        - Installer Update to address installation on Linux.
        
        ##### RNG 1.6.6
        - Documentation Update
        
        ##### RNG 1.6.5
        - Fixed Typos
        
        ##### RNG 1.6.4
        - Installer update.
        
        ##### RNG 1.6.3
        - More minor typos fixed.
        
        ##### RNG 1.6.2
        - Minor typos fixed.
        
        ##### RNG 1.6.1
        - Storm 3.2.2 Update.
        
        ##### 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
        ```
        MonkeyScope: RNG Tests
        =========================================================================
        
        Boolean Variate Distributions
        
        Output Analysis: bernoulli_variate(0.0)
        Typical Timing: 36 ± 5 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: 0.314
         Std Deviation: 0.46411636471902173
        Distribution of 10000 samples:
         False: 66.93%
         True: 33.07%
        
        Output Analysis: bernoulli_variate(0.5)
        Typical Timing: 40 ± 5 ns
        Statistics of 1000 samples:
         Minimum: False
         Median: False
         Maximum: True
         Mean: 0.494
         Std Deviation: 0.4999639987039066
        Distribution of 10000 samples:
         False: 50.59%
         True: 49.41%
        
        Output Analysis: bernoulli_variate(0.6666666666666666)
        Typical Timing: 40 ± 5 ns
        Statistics of 1000 samples:
         Minimum: False
         Median: True
         Maximum: True
         Mean: 0.684
         Std Deviation: 0.46491289506745237
        Distribution of 10000 samples:
         False: 33.22%
         True: 66.78%
        
        Output Analysis: bernoulli_variate(1.0)
        Typical Timing: 32 ± 1 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: 1088 ± 61 ns
        Statistics of 1000 samples:
         Minimum: 1
         Median: 4
         Maximum: 6
         Mean: 3.532
         Std Deviation: 1.6914419883637746
        Distribution of 10000 samples:
         1: 16.79%
         2: 16.93%
         3: 16.16%
         4: 16.82%
         5: 16.18%
         6: 17.12%
        
        Output Analysis: uniform_int_variate(1, 6)
        Typical Timing: 59 ± 8 ns
        Statistics of 1000 samples:
         Minimum: 1
         Median: 3
         Maximum: 6
         Mean: 3.426
         Std Deviation: 1.7408400271133475
        Distribution of 10000 samples:
         1: 17.01%
         2: 16.39%
         3: 16.66%
         4: 16.5%
         5: 16.71%
         6: 16.73%
        
        Output Analysis: binomial_variate(4, 0.5)
        Typical Timing: 140 ± 13 ns
        Statistics of 1000 samples:
         Minimum: 0
         Median: 2
         Maximum: 4
         Mean: 2.038
         Std Deviation: 0.984152427218467
        Distribution of 10000 samples:
         0: 6.12%
         1: 25.1%
         2: 37.29%
         3: 25.0%
         4: 6.49%
        
        Output Analysis: negative_binomial_variate(5, 0.75)
        Typical Timing: 118 ± 6 ns
        Statistics of 1000 samples:
         Minimum: 0
         Median: 1
         Maximum: 8
         Mean: 1.685
         Std Deviation: 1.4885479501850116
        Distribution of 10000 samples:
         0: 23.35%
         1: 29.92%
         2: 22.14%
         3: 12.89%
         4: 6.53%
         5: 2.98%
         6: 1.4%
         7: 0.49%
         8: 0.17%
         9: 0.06%
         10: 0.02%
         11: 0.04%
         13: 0.01%
        
        Output Analysis: geometric_variate(0.75)
        Typical Timing: 47 ± 4 ns
        Statistics of 1000 samples:
         Minimum: 0
         Median: 0
         Maximum: 5
         Mean: 0.316
         Std Deviation: 0.6404248589803491
        Distribution of 10000 samples:
         0: 75.33%
         1: 18.4%
         2: 4.72%
         3: 1.12%
         4: 0.34%
         5: 0.07%
         6: 0.01%
         7: 0.01%
        
        Output Analysis: poisson_variate(4.5)
        Typical Timing: 110 ± 6 ns
        Statistics of 1000 samples:
         Minimum: 0
         Median: 4
         Maximum: 12
         Mean: 4.454
         Std Deviation: 2.1042537869753257
        Distribution of 10000 samples:
         0: 1.16%
         1: 5.0%
         2: 11.72%
         3: 16.76%
         4: 18.74%
         5: 17.24%
         6: 12.49%
         7: 8.34%
         8: 4.72%
         9: 2.38%
         10: 0.83%
         11: 0.41%
         12: 0.14%
         13: 0.05%
         14: 0.02%
        
        
        Floating Point Variate Distributions
        
        Base Case
        Output Analysis: Random.random()
        Typical Timing: 32 ± 1 ns
        Statistics of 1000 samples:
         Minimum: 0.00020470717953002815
         Median: (0.4902766190643588, 0.49287975190577293)
         Maximum: 0.9998379746872317
         Mean: 0.4932275568514134
         Std Deviation: 0.2943772077006881
        Post-processor distribution of 10000 samples using round method:
         0: 50.0%
         1: 50.0%
        
        Output Analysis: generate_canonical()
        Typical Timing: 38 ± 4 ns
        Statistics of 1000 samples:
         Minimum: 0.0018313969488656262
         Median: (0.4999019745098515, 0.5000103956471701)
         Maximum: 0.9979157465022215
         Mean: 0.4983743559000373
         Std Deviation: 0.2857287394134513
        Post-processor distribution of 10000 samples using round method:
         0: 50.0%
         1: 50.0%
        
        Base Case
        Output Analysis: Random.uniform(0.0, 10.0)
        Typical Timing: 229 ± 16 ns
        Statistics of 1000 samples:
         Minimum: 0.0011475989233244999
         Median: (4.686759881204559, 4.698175014514612)
         Maximum: 9.994578954522886
         Mean: 4.90558557056895
         Std Deviation: 2.8901372998841577
        Post-processor distribution of 10000 samples using floor method:
         0: 9.58%
         1: 10.5%
         2: 10.06%
         3: 10.09%
         4: 10.19%
         5: 9.98%
         6: 10.21%
         7: 9.76%
         8: 10.05%
         9: 9.58%
        
        Output Analysis: uniform_real_variate(0.0, 10.0)
        Typical Timing: 41 ± 7 ns
        Statistics of 1000 samples:
         Minimum: 0.003871772166954936
         Median: (4.975709613518741, 4.99955707001919)
         Maximum: 9.99847163973721
         Mean: 5.041222981691081
         Std Deviation: 2.8720806186652457
        Post-processor distribution of 10000 samples using floor method:
         0: 10.33%
         1: 9.62%
         2: 10.08%
         3: 10.11%
         4: 9.57%
         5: 10.12%
         6: 9.88%
         7: 9.97%
         8: 10.53%
         9: 9.79%
        
        Base Case
        Output Analysis: Random.expovariate(1.0)
        Typical Timing: 336 ± 21 ns
        Statistics of 1000 samples:
         Minimum: 0.0008104371505790905
         Median: (0.6999357838616548, 0.7018251058756864)
         Maximum: 7.266089757629259
         Mean: 1.0321635246736387
         Std Deviation: 1.084028320793121
        Post-processor distribution of 10000 samples using floor method:
         0: 63.7%
         1: 22.72%
         2: 8.33%
         3: 3.3%
         4: 1.26%
         5: 0.4%
         6: 0.22%
         7: 0.04%
         8: 0.01%
         9: 0.01%
         11: 0.01%
        
        Output Analysis: exponential_variate(1.0)
        Typical Timing: 58 ± 8 ns
        Statistics of 1000 samples:
         Minimum: 0.00018170037869458518
         Median: (0.7377113850720796, 0.7382275278647656)
         Maximum: 7.084870250885541
         Mean: 1.0149957923408772
         Std Deviation: 0.9661594059665878
        Post-processor distribution of 10000 samples using floor method:
         0: 63.61%
         1: 23.32%
         2: 8.32%
         3: 3.09%
         4: 1.22%
         5: 0.26%
         6: 0.07%
         7: 0.09%
         8: 0.01%
         10: 0.01%
        
        Base Case
        Output Analysis: Random.gammavariate(1.0, 1.0)
        Typical Timing: 487 ± 38 ns
        Statistics of 1000 samples:
         Minimum: 0.0010269090045949092
         Median: (0.6758922146292059, 0.6764364701429871)
         Maximum: 6.62473131254665
         Mean: 0.9526441065384418
         Std Deviation: 0.9235697791084873
        Post-processor distribution of 10000 samples using floor method:
         0: 62.38%
         1: 23.78%
         2: 8.78%
         3: 3.13%
         4: 1.15%
         5: 0.51%
         6: 0.15%
         7: 0.08%
         8: 0.03%
         9: 0.01%
        
        Output Analysis: gamma_variate(1.0, 1.0)
        Typical Timing: 59 ± 6 ns
        Statistics of 1000 samples:
         Minimum: 7.943300227810338e-05
         Median: (0.7410362381108585, 0.7486551841969276)
         Maximum: 9.45441577468242
         Mean: 1.0319939318688074
         Std Deviation: 1.0133677265428946
        Post-processor distribution of 10000 samples using floor method:
         0: 63.46%
         1: 23.11%
         2: 8.67%
         3: 3.01%
         4: 1.18%
         5: 0.37%
         6: 0.08%
         7: 0.07%
         8: 0.03%
         9: 0.02%
        
        Base Case
        Output Analysis: Random.weibullvariate(1.0, 1.0)
        Typical Timing: 429 ± 28 ns
        Statistics of 1000 samples:
         Minimum: 0.00014457030695612128
         Median: (0.6976139576861516, 0.6988385606862981)
         Maximum: 7.441929633011179
         Mean: 1.0360647371223919
         Std Deviation: 1.0423724124307105
        Post-processor distribution of 10000 samples using floor method:
         0: 62.26%
         1: 23.95%
         2: 8.67%
         3: 3.29%
         4: 1.1%
         5: 0.43%
         6: 0.17%
         7: 0.08%
         8: 0.03%
         9: 0.02%
        
        Output Analysis: weibull_variate(1.0, 1.0)
        Typical Timing: 97 ± 11 ns
        Statistics of 1000 samples:
         Minimum: 0.0001784696086844487
         Median: (0.6941698928066924, 0.6948215528368951)
         Maximum: 8.369567364716502
         Mean: 0.9966718438000562
         Std Deviation: 0.9674836941611771
        Post-processor distribution of 10000 samples using floor method:
         0: 63.68%
         1: 23.0%
         2: 8.41%
         3: 3.08%
         4: 1.33%
         5: 0.28%
         6: 0.16%
         7: 0.03%
         8: 0.03%
        
        Output Analysis: extreme_value_variate(0.0, 1.0)
        Typical Timing: 79 ± 9 ns
        Statistics of 1000 samples:
         Minimum: -2.0871331458605598
         Median: (0.3869710970824426, 0.3904990435365016)
         Maximum: 6.870642194863319
         Mean: 0.5738754165001632
         Std Deviation: 1.2729399422463183
        Post-processor distribution of 10000 samples using round method:
         -2: 1.2%
         -1: 18.28%
         0: 35.12%
         1: 26.2%
         2: 11.5%
         3: 4.59%
         4: 2.04%
         5: 0.62%
         6: 0.24%
         7: 0.11%
         8: 0.07%
         9: 0.03%
        
        Base Case
        Output Analysis: Random.gauss(5.0, 2.0)
        Typical Timing: 579 ± 4 ns
        Statistics of 1000 samples:
         Minimum: -1.0864435352344746
         Median: (4.896021009703201, 4.8960635966136605)
         Maximum: 11.016252215240552
         Mean: 4.965365989325721
         Std Deviation: 1.9758300884950102
        Post-processor distribution of 10000 samples using round method:
         -2: 0.04%
         -1: 0.21%
         0: 0.82%
         1: 2.82%
         2: 6.33%
         3: 11.97%
         4: 17.45%
         5: 19.95%
         6: 17.63%
         7: 12.03%
         8: 6.67%
         9: 2.89%
         10: 0.96%
         11: 0.21%
         12: 0.02%
        
        Output Analysis: normal_variate(5.0, 2.0)
        Typical Timing: 87 ± 2 ns
        Statistics of 1000 samples:
         Minimum: -2.405912295794069
         Median: (5.0738045445174995, 5.082279716366891)
         Maximum: 12.381509481018478
         Mean: 5.092836336120188
         Std Deviation: 2.014023813195181
        Post-processor distribution of 10000 samples using round method:
         -2: 0.06%
         -1: 0.28%
         0: 0.79%
         1: 2.77%
         2: 6.21%
         3: 12.0%
         4: 17.38%
         5: 19.86%
         6: 17.04%
         7: 13.02%
         8: 6.47%
         9: 3.01%
         10: 0.83%
         11: 0.22%
         12: 0.05%
         13: 0.01%
        
        Base Case
        Output Analysis: Random.lognormvariate(1.6, 0.25)
        Typical Timing: 871 ± 28 ns
        Statistics of 1000 samples:
         Minimum: 2.1400232821020095
         Median: (4.970649274737473, 4.972897691731108)
         Maximum: 11.584234487161941
         Mean: 5.099080347282304
         Std Deviation: 1.2828682146566066
        Post-processor distribution of 10000 samples using round method:
         2: 0.35%
         3: 7.51%
         4: 27.79%
         5: 30.41%
         6: 19.95%
         7: 9.09%
         8: 3.4%
         9: 0.96%
         10: 0.43%
         11: 0.07%
         12: 0.03%
         14: 0.01%
        
        Output Analysis: lognormal_variate(1.6, 0.25)
        Typical Timing: 111 ± 10 ns
        Statistics of 1000 samples:
         Minimum: 2.0565509130084196
         Median: (4.951319938162054, 4.956358747365627)
         Maximum: 10.705411301927128
         Mean: 5.084514398741803
         Std Deviation: 1.2702879123571864
        Post-processor distribution of 10000 samples using round method:
         2: 0.23%
         3: 7.93%
         4: 26.34%
         5: 31.23%
         6: 20.45%
         7: 9.02%
         8: 3.25%
         9: 1.12%
         10: 0.33%
         11: 0.08%
         12: 0.02%
        
        Output Analysis: chi_squared_variate(1.0)
        Typical Timing: 123 ± 14 ns
        Statistics of 1000 samples:
         Minimum: 4.560050044340425e-07
         Median: (0.43843953531720004, 0.44248536881992023)
         Maximum: 12.827094790848676
         Mean: 1.0076909905738598
         Std Deviation: 1.4661644618564504
        Post-processor distribution of 10000 samples using floor method:
         0: 68.66%
         1: 15.68%
         2: 7.28%
         3: 3.75%
         4: 2.04%
         5: 1.08%
         6: 0.69%
         7: 0.35%
         8: 0.2%
         9: 0.09%
         10: 0.1%
         11: 0.02%
         12: 0.03%
         13: 0.01%
         15: 0.02%
        
        Output Analysis: cauchy_variate(0.0, 1.0)
        Typical Timing: 81 ± 7 ns
        Statistics of 1000 samples:
         Minimum: -80.60257833512122
         Median: (-0.07136445696912773, -0.06912576811779096)
         Maximum: 302.9285259983777
         Mean: 0.048158350818380484
         Std Deviation: 14.33030682036316
        Post-processor distribution of 10000 samples using floor_mod_10 method:
         0: 25.9%
         1: 11.52%
         2: 5.6%
         3: 3.76%
         4: 3.18%
         5: 3.41%
         6: 3.9%
         7: 5.8%
         8: 10.97%
         9: 25.96%
        
        Output Analysis: fisher_f_variate(8.0, 8.0)
        Typical Timing: 201 ± 18 ns
        Statistics of 1000 samples:
         Minimum: 0.06789103397534023
         Median: (0.9903987592191404, 0.990587816520427)
         Maximum: 11.194291185158521
         Mean: 1.2913804655586418
         Std Deviation: 1.0772469766917976
        Post-processor distribution of 10000 samples using floor method:
         0: 50.15%
         1: 32.35%
         2: 10.29%
         3: 3.99%
         4: 1.61%
         5: 0.67%
         6: 0.31%
         7: 0.19%
         8: 0.1%
         9: 0.14%
         10: 0.05%
         11: 0.05%
         12: 0.02%
         13: 0.03%
         14: 0.01%
         15: 0.01%
         16: 0.01%
         18: 0.01%
         106: 0.01%
        
        Output Analysis: student_t_variate(8.0)
        Typical Timing: 164 ± 12 ns
        Statistics of 1000 samples:
         Minimum: -5.182813039237713
         Median: (-0.040463896877030454, -0.03931607130970538)
         Maximum: 5.338481501612407
         Mean: 0.005453093050598653
         Std Deviation: 1.1905125047921103
        Post-processor distribution of 10000 samples using round method:
         -7: 0.01%
         -6: 0.03%
         -5: 0.05%
         -4: 0.36%
         -3: 1.4%
         -2: 6.6%
         -1: 23.55%
         0: 36.53%
         1: 23.14%
         2: 6.39%
         3: 1.42%
         4: 0.34%
         5: 0.14%
         6: 0.02%
         7: 0.02%
        
        
        =========================================================================
        Total Test Time: 0.5327 seconds
        
        ```
        
Keywords: rng,Mersenne Twister,random number generator,cpp random library,random integer,Bernoulli,binomial,negative binomial,geometric,poisson,discrete,normal,distribution,log normal,gamma,exponential,weibull,extreme value,chi squared,cauchy,fisher f,student t
Platform: Darwin
Platform: Linux
Classifier: Development Status :: 5 - Production/Stable
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Cython
Classifier: Programming Language :: C++
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX :: Linux
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires: Cython
Requires-Python: >=3.6
Description-Content-Type: text/markdown
