Metadata-Version: 2.1
Name: RNG
Version: 1.5.1
Summary: Python3 API for the C++ Random Library
Home-page: https://sharpdesigndigital.com
Author: Robert Sharp
Author-email: webmaster@sharpdesigndigital.com
License: Free for non-commercial use
Description: # RNG Engine for Python3
        - Python3 interface to the c++ random library
        - Designed for python developers familiar with the c++ random header
        - Warning: RNG is not suitable for cryptography or secure hashing.
        
        ### Quick Install for Mac
        ``` 
        $ pip install RNG
        $ python3
        Python 3.7.3
        >>> import RNG
        >>> RNG.generate_canonical()
        0.39652726016896334
        ```
        ### Installation may require the following:
        - Python 3.7 or later.
        - Cython, python module available: `pip install Cython`
        - Python3 developer environment, setuptools etc.
        - Modern C++17 Compiler and Standard Library, Clang or GCC.
        
        ### Sister Projects:
        - Fortuna: Collection of abstractions to make custom random value generators. https://pypi.org/project/Fortuna/
        - Pyewacket: Complete drop-in replacement for the Python3 random module. https://pypi.org/project/Pyewacket/
        - MonkeyTimer: Framework for testing non-deterministic value generators. https://pypi.org/project/MonkeyTimer/
        
        Support these and other random projects: https://www.patreon.com/brokencode
        
        ---
        
        ## 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
        ```python
        # bernoulli_variate.py
        from RNG import bernoulli_variate
        
        
        print(bernoulli_variate(0.25))
        # prints a random boolean, 25% probability of True
        ```
        
        #### 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
        ```python
        # uniform_int_variate.py
        from RNG import uniform_int_variate
        
        
        print(uniform_int_variate(-6, 5))
        # prints a random int in range [-6, 5]
        ```
        
        - `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)
        ```python
        # generate_canonical.py
        from RNG import generate_canonical
        
        
        print(generate_canonical())
        # prints a 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 1.5.1
        - A number of testing routines have been extracted into a new module: MonkeyTimer.
            - 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.
        
        
        ## MonkeyTimer: Distribution and Performance Test Suite
        ```
        Quick Test: RNG Storm Engine
        =========================================================================
        
        Boolean Variate Distributions
        
        Output Analysis: bernoulli_variate(0.0)
        Typical Timing: 32 ± 12 ns
        Statistics of 1024 samples:
         Minimum: False
         Median: False
         Maximum: False
         Mean: 0
         Std Deviation: 0.0
        Distribution of 10240 samples:
         False: 100.0%
        
        Output Analysis: bernoulli_variate(0.3333333333333333)
        Typical Timing: 63 ± 1 ns
        Statistics of 1024 samples:
         Minimum: False
         Median: False
         Maximum: True
         Mean: 0.34375
         Std Deviation: 0.47519096331149147
        Distribution of 10240 samples:
         False: 66.201171875%
         True: 33.798828125%
        
        Output Analysis: bernoulli_variate(0.5)
        Typical Timing: 63 ± 1 ns
        Statistics of 1024 samples:
         Minimum: False
         Median: True
         Maximum: True
         Mean: 0.521484375
         Std Deviation: 0.4997823023144626
        Distribution of 10240 samples:
         False: 49.0625%
         True: 50.9375%
        
        Output Analysis: bernoulli_variate(0.6666666666666666)
        Typical Timing: 63 ± 1 ns
        Statistics of 1024 samples:
         Minimum: False
         Median: True
         Maximum: True
         Mean: 0.6513671875
         Std Deviation: 0.4767703398158829
        Distribution of 10240 samples:
         False: 32.412109375%
         True: 67.587890625%
        
        Output Analysis: bernoulli_variate(1.0)
        Typical Timing: 32 ± 16 ns
        Statistics of 1024 samples:
         Minimum: True
         Median: True
         Maximum: True
         Mean: 1
         Std Deviation: 0.0
        Distribution of 10240 samples:
         True: 100.0%
        
        
        Integer Variate Distributions
        
        Base Case
        Output Analysis: Random.randint(1, 6)
        Typical Timing: 1188 ± 18 ns
        Statistics of 1024 samples:
         Minimum: 1
         Median: 3
         Maximum: 6
         Mean: 3.51171875
         Std Deviation: 1.7164204309541053
        Distribution of 10240 samples:
         1: 16.748046875%
         2: 16.748046875%
         3: 16.7578125%
         4: 16.171875%
         5: 17.119140625%
         6: 16.455078125%
        
        Output Analysis: uniform_int_variate(1, 6)
        Typical Timing: 63 ± 13 ns
        Statistics of 1024 samples:
         Minimum: 1
         Median: 3
         Maximum: 6
         Mean: 3.49609375
         Std Deviation: 1.706747111851172
        Distribution of 10240 samples:
         1: 17.216796875%
         2: 16.58203125%
         3: 16.640625%
         4: 16.201171875%
         5: 16.89453125%
         6: 16.46484375%
        
        Output Analysis: binomial_variate(4, 0.5)
        Typical Timing: 125 ± 6 ns
        Statistics of 1024 samples:
         Minimum: 0
         Median: 2
         Maximum: 4
         Mean: 1.990234375
         Std Deviation: 0.9876571326534981
        Distribution of 10240 samples:
         0: 5.83984375%
         1: 25.771484375%
         2: 37.021484375%
         3: 24.990234375%
         4: 6.376953125%
        
        Output Analysis: negative_binomial_variate(5, 0.75)
        Typical Timing: 125 ± 6 ns
        Statistics of 1024 samples:
         Minimum: 0
         Median: 1
         Maximum: 9
         Mean: 1.642578125
         Std Deviation: 1.4317935739712264
        Distribution of 10240 samples:
         0: 23.505859375%
         1: 30.048828125%
         2: 21.9140625%
         3: 13.046875%
         4: 6.806640625%
         5: 2.744140625%
         6: 1.220703125%
         7: 0.458984375%
         8: 0.17578125%
         9: 0.05859375%
         10: 0.01953125%
        
        Output Analysis: geometric_variate(0.75)
        Typical Timing: 63 ± 1 ns
        Statistics of 1024 samples:
         Minimum: 0
         Median: 0
         Maximum: 6
         Mean: 0.3349609375
         Std Deviation: 0.6949815910960272
        Distribution of 10240 samples:
         0: 74.892578125%
         1: 18.57421875%
         2: 4.94140625%
         3: 1.142578125%
         4: 0.29296875%
         5: 0.078125%
         6: 0.05859375%
         7: 0.01953125%
        
        Output Analysis: poisson_variate(4.5)
        Typical Timing: 125 ± 1 ns
        Statistics of 1024 samples:
         Minimum: 0
         Median: 4
         Maximum: 14
         Mean: 4.5224609375
         Std Deviation: 2.161487169159546
        Distribution of 10240 samples:
         0: 1.25%
         1: 4.6875%
         2: 10.986328125%
         3: 17.197265625%
         4: 18.28125%
         5: 16.826171875%
         6: 13.02734375%
         7: 8.662109375%
         8: 4.853515625%
         9: 2.40234375%
         10: 1.005859375%
         11: 0.546875%
         12: 0.166015625%
         13: 0.078125%
         14: 0.029296875%
        
        
        Floating Point Variate Distributions
        
        Base Case
        Output Analysis: Random.random()
        Typical Timing: 32 ± 16 ns
        Statistics of 1024 samples:
         Minimum: 0.0006388470747341612
         Median: (0.4583390582236725, 0.4589449537736383)
         Maximum: 0.9998833078220133
         Mean: 0.47560204568672454
         Std Deviation: 0.28999057863750183
        Post-processor distribution of 10240 samples using round method:
         0: 50.693359375%
         1: 49.306640625%
        
        Output Analysis: generate_canonical()
        Typical Timing: 32 ± 16 ns
        Statistics of 1024 samples:
         Minimum: 0.0004296508719445195
         Median: (0.4917702846481638, 0.49188511675337104)
         Maximum: 0.9978423639409355
         Mean: 0.49181897826571025
         Std Deviation: 0.2876430386292914
        Post-processor distribution of 10240 samples using round method:
         0: 49.658203125%
         1: 50.341796875%
        
        Output Analysis: uniform_real_variate(0.0, 10.0)
        Typical Timing: 94 ± 12 ns
        Statistics of 1024 samples:
         Minimum: 0.0019713906008227973
         Median: (5.0088507641964055, 5.009507796219312)
         Maximum: 9.987524245931592
         Mean: 5.064104214610925
         Std Deviation: 2.9217505674564164
        Post-processor distribution of 10240 samples using floor method:
         0: 9.94140625%
         1: 10.234375%
         2: 10.15625%
         3: 10.380859375%
         4: 9.951171875%
         5: 10.01953125%
         6: 9.873046875%
         7: 9.62890625%
         8: 9.931640625%
         9: 9.8828125%
        
        Base Case
        Output Analysis: Random.expovariate(1.0)
        Typical Timing: 344 ± 10 ns
        Statistics of 1024 samples:
         Minimum: 8.208047312447906e-05
         Median: (0.6686009795871479, 0.6721875885943366)
         Maximum: 7.8351444976253335
         Mean: 0.9571749101061867
         Std Deviation: 0.9753799224623942
        Post-processor distribution of 10240 samples using floor method:
         0: 63.310546875%
         1: 23.076171875%
         2: 8.681640625%
         3: 3.02734375%
         4: 1.083984375%
         5: 0.576171875%
         6: 0.146484375%
         7: 0.048828125%
         8: 0.01953125%
         9: 0.009765625%
         11: 0.01953125%
        
        Output Analysis: exponential_variate(1.0)
        Typical Timing: 63 ± 10 ns
        Statistics of 1024 samples:
         Minimum: 0.0013282608302530423
         Median: (0.6690274531506568, 0.669316218295648)
         Maximum: 8.748834094909625
         Mean: 1.0318402860279123
         Std Deviation: 1.0548016848406403
        Post-processor distribution of 10240 samples using floor method:
         0: 63.61328125%
         1: 22.587890625%
         2: 8.603515625%
         3: 3.310546875%
         4: 1.162109375%
         5: 0.46875%
         6: 0.17578125%
         7: 0.0390625%
         8: 0.01953125%
         9: 0.01953125%
        
        Base Case
        Output Analysis: Random.gammavariate(1.0, 1.0)
        Typical Timing: 469 ± 16 ns
        Statistics of 1024 samples:
         Minimum: 0.0006029624140358793
         Median: (0.7315241311192433, 0.7353963314603675)
         Maximum: 7.196638281972511
         Mean: 1.0011919746296876
         Std Deviation: 0.9634904644142317
        Post-processor distribution of 10240 samples using floor method:
         0: 64.130859375%
         1: 22.03125%
         2: 8.80859375%
         3: 3.154296875%
         4: 1.240234375%
         5: 0.41015625%
         6: 0.126953125%
         7: 0.05859375%
         8: 0.029296875%
         10: 0.009765625%
        
        Output Analysis: gamma_variate(1.0, 1.0)
        Typical Timing: 63 ± 11 ns
        Statistics of 1024 samples:
         Minimum: 0.0005988820827224266
         Median: (0.6857081774501348, 0.686356057608084)
         Maximum: 7.263998659948883
         Mean: 0.9806944084458747
         Std Deviation: 0.9622234373186321
        Post-processor distribution of 10240 samples using floor method:
         0: 63.0859375%
         1: 23.4765625%
         2: 8.955078125%
         3: 2.8515625%
         4: 0.9375%
         5: 0.44921875%
         6: 0.13671875%
         7: 0.09765625%
         8: 0.009765625%
        
        Base Case
        Output Analysis: Random.weibullvariate(1.0, 1.0)
        Typical Timing: 407 ± 16 ns
        Statistics of 1024 samples:
         Minimum: 3.0687715901322686e-05
         Median: (0.668937878075442, 0.6701855697870736)
         Maximum: 5.840822106883279
         Mean: 0.9672608159897019
         Std Deviation: 0.9480419472328683
        Post-processor distribution of 10240 samples using floor method:
         0: 63.408203125%
         1: 23.0078125%
         2: 8.65234375%
         3: 3.251953125%
         4: 1.11328125%
         5: 0.3515625%
         6: 0.126953125%
         7: 0.068359375%
         8: 0.01953125%
        
        Output Analysis: weibull_variate(1.0, 1.0)
        Typical Timing: 94 ± 15 ns
        Statistics of 1024 samples:
         Minimum: 0.00014086748786664374
         Median: (0.6945683032536986, 0.6982508879406859)
         Maximum: 5.80290970777546
         Mean: 0.9892732402317378
         Std Deviation: 0.9445212781958421
        Post-processor distribution of 10240 samples using floor method:
         0: 62.8125%
         1: 23.828125%
         2: 8.80859375%
         3: 2.8515625%
         4: 1.005859375%
         5: 0.44921875%
         6: 0.185546875%
         7: 0.01953125%
         8: 0.01953125%
         9: 0.01953125%
        
        Output Analysis: extreme_value_variate(0.0, 1.0)
        Typical Timing: 63 ± 11 ns
        Statistics of 1024 samples:
         Minimum: -2.2549353788055053
         Median: (0.3760487699667133, 0.3766491002847829)
         Maximum: 7.953843598096162
         Mean: 0.572674798704818
         Std Deviation: 1.2480372336144154
        Post-processor distribution of 10240 samples using round method:
         -2: 1.0546875%
         -1: 17.919921875%
         0: 35.400390625%
         1: 25.205078125%
         2: 12.51953125%
         3: 5.15625%
         4: 1.669921875%
         5: 0.615234375%
         6: 0.263671875%
         7: 0.126953125%
         8: 0.048828125%
         9: 0.009765625%
         10: 0.009765625%
        
        Base Case
        Output Analysis: Random.gauss(5.0, 2.0)
        Typical Timing: 594 ± 12 ns
        Statistics of 1024 samples:
         Minimum: -1.5155551600414325
         Median: (4.900045625318894, 4.910640304340043)
         Maximum: 10.074285309328179
         Mean: 4.903701264243073
         Std Deviation: 1.9284195623352982
        Post-processor distribution of 10240 samples using round method:
         -3: 0.009765625%
         -2: 0.09765625%
         -1: 0.15625%
         0: 1.044921875%
         1: 2.802734375%
         2: 6.513671875%
         3: 11.865234375%
         4: 17.36328125%
         5: 20.126953125%
         6: 17.861328125%
         7: 11.669921875%
         8: 6.650390625%
         9: 2.71484375%
         10: 0.830078125%
         11: 0.244140625%
         12: 0.0390625%
         13: 0.009765625%
        
        Output Analysis: normal_variate(5.0, 2.0)
        Typical Timing: 94 ± 11 ns
        Statistics of 1024 samples:
         Minimum: -1.6386997496778761
         Median: (4.893535541707389, 4.8952247982158745)
         Maximum: 11.47237356973277
         Mean: 4.989727450891901
         Std Deviation: 2.0086474955642575
        Post-processor distribution of 10240 samples using round method:
         -2: 0.05859375%
         -1: 0.224609375%
         0: 0.869140625%
         1: 2.392578125%
         2: 6.865234375%
         3: 11.943359375%
         4: 17.6953125%
         5: 19.443359375%
         6: 17.216796875%
         7: 12.041015625%
         8: 7.138671875%
         9: 2.783203125%
         10: 1.015625%
         11: 0.25390625%
         12: 0.048828125%
         13: 0.009765625%
        
        Base Case
        Output Analysis: Random.lognormvariate(1.6, 0.25)
        Typical Timing: 844 ± 37 ns
        Statistics of 1024 samples:
         Minimum: 2.477424724086346
         Median: (4.900857180539914, 4.901204763837707)
         Maximum: 12.011155123497085
         Mean: 5.052798380724913
         Std Deviation: 1.2653879254348632
        Post-processor distribution of 10240 samples using round method:
         2: 0.244140625%
         3: 8.330078125%
         4: 26.240234375%
         5: 31.220703125%
         6: 20.17578125%
         7: 9.111328125%
         8: 3.251953125%
         9: 1.015625%
         10: 0.322265625%
         11: 0.068359375%
         12: 0.009765625%
         13: 0.009765625%
        
        Output Analysis: lognormal_variate(1.6, 0.25)
        Typical Timing: 94 ± 6 ns
        Statistics of 1024 samples:
         Minimum: 2.3183785808960096
         Median: (4.874986177263607, 4.876857798124825)
         Maximum: 11.730007308909611
         Mean: 5.0532590833629625
         Std Deviation: 1.2985639553150765
        Post-processor distribution of 10240 samples using round method:
         2: 0.302734375%
         3: 8.30078125%
         4: 26.9140625%
         5: 30.8203125%
         6: 19.609375%
         7: 9.08203125%
         8: 3.49609375%
         9: 1.025390625%
         10: 0.37109375%
         11: 0.048828125%
         12: 0.029296875%
        
        Output Analysis: chi_squared_variate(1.0)
        Typical Timing: 125 ± 13 ns
        Statistics of 1024 samples:
         Minimum: 4.936117433434296e-07
         Median: (0.4459156690762514, 0.4465653999002458)
         Maximum: 10.791875060714037
         Mean: 1.044424368814932
         Std Deviation: 1.5011087023219227
        Post-processor distribution of 10240 samples using floor method:
         0: 68.37890625%
         1: 15.91796875%
         2: 7.6171875%
         3: 3.59375%
         4: 1.787109375%
         5: 1.19140625%
         6: 0.625%
         7: 0.419921875%
         8: 0.244140625%
         9: 0.068359375%
         10: 0.068359375%
         11: 0.048828125%
         12: 0.029296875%
         13: 0.009765625%
        
        Output Analysis: cauchy_variate(0.0, 1.0)
        Typical Timing: 63 ± 8 ns
        Statistics of 1024 samples:
         Minimum: -470.67404141176274
         Median: (0.07716803089648394, 0.0842124948591796)
         Maximum: 1719.0816436410664
         Mean: 0.665703585009031
         Std Deviation: 59.31979062261256
        Post-processor distribution of 10240 samples using floor_mod_10 method:
         0: 26.03515625%
         1: 11.083984375%
         2: 5.91796875%
         3: 3.994140625%
         4: 3.232421875%
         5: 2.98828125%
         6: 3.73046875%
         7: 5.48828125%
         8: 11.69921875%
         9: 25.830078125%
        
        Output Analysis: fisher_f_variate(8.0, 8.0)
        Typical Timing: 188 ± 14 ns
        Statistics of 1024 samples:
         Minimum: 0.05459418873515563
         Median: (0.9919763938480465, 0.9943128957909915)
         Maximum: 12.175780106763689
         Mean: 1.329020084097388
         Std Deviation: 1.209412184995222
        Post-processor distribution of 10240 samples using floor method:
         0: 50.361328125%
         1: 32.5390625%
         2: 10.25390625%
         3: 3.642578125%
         4: 1.484375%
         5: 0.771484375%
         6: 0.380859375%
         7: 0.244140625%
         8: 0.068359375%
         9: 0.078125%
         10: 0.0390625%
         11: 0.029296875%
         12: 0.0390625%
         13: 0.01953125%
         14: 0.01953125%
         16: 0.009765625%
         17: 0.009765625%
         25: 0.009765625%
        
        Output Analysis: student_t_variate(8.0)
        Typical Timing: 157 ± 14 ns
        Statistics of 1024 samples:
         Minimum: -3.6063905568291186
         Median: (-0.023436101947007702, -0.02125804725939844)
         Maximum: 5.463724716098776
         Mean: 0.03336687922475384
         Std Deviation: 1.158115050869326
        Post-processor distribution of 10240 samples using round method:
         -6: 0.0390625%
         -5: 0.087890625%
         -4: 0.302734375%
         -3: 1.533203125%
         -2: 6.73828125%
         -1: 23.125%
         0: 36.953125%
         1: 22.626953125%
         2: 6.728515625%
         3: 1.4453125%
         4: 0.322265625%
         5: 0.078125%
         6: 0.01953125%
        
        
        =========================================================================
        Total Test Time: 0.5838 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: >=3.7
Description-Content-Type: text/markdown
