Metadata-Version: 2.1
Name: RNG
Version: 1.3.3
Summary: Python API for the C++ Random library
Home-page: https://sharpdesigndigital.com
Author: Broken aka Robert Sharp
Author-email: webmaster@sharpdesigndigital.com
License: Free for non-commercial use
Description: # Random Number Generator: RNG Storm Engine
        
        Python API for the C++ Random library.
        
        **RNG is not suitable for cryptography, but it could be perfect for other random stuff like data science, experimental programming, A.I. and games.**
        
        
        *Recommended Installation:* `$ pip install RNG`
        
        Support this project: https://www.patreon.com/brokencode
        
        
        Number Types, Precision & Size:
        - Float: Python float -> double at the C++ layer.
            - Min Float: -1.7976931348623157e+308
            - Max Float:  1.7976931348623157e+308
            - Min Below Zero: -5e-324
            - Min Above Zero:  5e-324
        
        - Integer: Python int -> long long at the C++ layer.
            - Input & Output Range: `(-2**63, 2**63)` or approximately +/- 9.2 billion billion.
            - Min Integer: -9223372036854775807
            - Max Integer:  9223372036854775807
        
        
        #### Random Binary Function
        - `bernoulli(ratio_of_truth: float) -> bool`
            - Bernoulli distribution.
            - @param ratio_of_truth :: the probability of True as a decimal. Expected input range: [0.0, 1.0], clamped.
            - @return :: True or False
        
        
        #### Random Integer Functions
        - `random_int(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
        - `random_below(upper_bound: int) -> int`
            - Flat uniform distribution.
            - @param upper_bound :: inout A
            - @return :: random integer in exclusive range [0, A) or (A, 0] if A < 0
        - `binomial(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.
        - `negative_binomial(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.
        - `geometric(probability: float) -> int`
            - Same as random_negative_binomial(1, probability). 
        - `poisson(mean: float) -> int`
            - @param mean :: sets the average output of the function.
            - @return :: random integer, poisson distribution centered on the mean.
        
        
        #### Random Floating Point Functions
        - `generate_canonical() -> float`
            - Evenly distributes real values of maximum precision.
            - @return :: random Float in range {0.0, 1.0} biclusive. The spec defines the output range to be [0.0, 1.0).
                - biclusive: feature/bug rendering the exclusivity of this function a bit more mysterious than desired. This is a known compiler bug.
        - `random_float(left_limit: float, right_limit: float) -> float`
            - Suffers from the same biclusive feature/bug noted for generate_canonical().
            - @param left_limit :: input A 
            - @param right_limit :: input B
            - @return :: random Float in range {A, B} biclusive. The spec defines the output range to be [A, B).
        - `normalvariate(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.
        - `lognormvariate(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.
        - `exponential(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.
        - `gammavariate(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.
        - `weibullvariate(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.
        - `extreme_value(location: float, scale: float) -> float`
            - Based on Extreme Value Theory. 
            - Used for statistical models of the magnitude of earthquakes and volcanoes.
        - `chi_squared(degrees_of_freedom: float) -> float`
            - Used with the Chi Squared Test and Null Hypotheses to test if sample data fits an expected distribution.
        - `cauchy(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.
        - `fisher_f(degrees_of_freedom_1: float, degrees_of_freedom_2: float) -> float`
            - F distributions often arise when comparing ratios of variances.
        - `student_t(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.
        
        
        #### Engines
        - `mersenne_twister_engine`: internal only
            - Implements 64 bit Mersenne twister algorithm. Default engine on most systems.
        - `linear_congruential_engine`: internal only
            - Implements linear congruential algorithm.
        - `subtract_with_carry_engine`: internal only
            - Implements a subtract-with-carry (lagged Fibonacci) algorithm.
        - `storm_engine`: internal only
            - RNG: Custom Engine
            - Default Standard
        
        
        #### Engine Adaptors
        Engine adaptors generate pseudo-random numbers using another random number engine as entropy source. They are generally used to alter the spectral characteristics of the underlying engine.
        - `discard_block_engine`: internal only
            - Discards some output of a random number engine.
        - `independent_bits_engine`: internal only
            - Packs the output of a random number engine into blocks of a specified number of bits.
        - `shuffle_order_engine`: internal only
            - Delivers the output of a random number engine in a different order.
        
        
        #### Seeds & Entropy Source
        - `random_device`: internal only
            - Non-deterministic uniform random bit generator, although implementations are allowed to implement random_device using a pseudo-random number engine if there is no support for non-deterministic random number generation.
        - `seed_seq`: internal only
            - General-purpose bias-eliminating scrambled seed sequence generator.
        
        
        #### Distribution & Performance Test Suite
        - `distribution_timer(func: staticmethod, *args, **kwargs) -> None`
            - For statistical analysis of non-deterministic numeric functions.
            - @param func :: Function method or lambda to analyze. `func(*args, **kwargs)`
            - @optional_kw num_cycles :: Total number of samples for distribution analysis.
            - @optional_kw post_processor :: Used to scale a large set of data into a smaller set of groupings.
        - `quick_test(n=10000) -> None` 
            - Runs a battery of tests for every random distribution function in the module.
            - @param n :: the total number of samples to collect for each test. Default: 10,000
        
        
        ## Development Log
        ##### 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.
        
        
        ## Distribution and Performance Test Suite
        ```
        Quick Test: RNG Storm Engine
        
        Round Trip Numeric Limits:
         Min Integer: -9223372036854775807
         Max Integer:  9223372036854775807
         Min Float: -1.7976931348623157e+308
         Max Float:  1.7976931348623157e+308
         Min Below Zero: -5e-324
         Min Above Zero:  5e-324
        
        
        Binary Tests
        
        Output Analysis: bernoulli(0.3333333333333333)
        Typical Timing: 63 ± 1 ns
        Statistics of 1000 Samples:
         Minimum: False
         Median: False
         Maximum: True
         Mean: 0.297
         Std Deviation: 0.4571651780264984
        Distribution of 10000 Samples:
         False: 67.62%
         True: 32.38%
        
        
        Integer Tests
        
        Base Case
        Output Analysis: Random.randint(1, 6)
        Typical Timing: 1157 ± 11 ns
        Statistics of 1000 Samples:
         Minimum: 1
         Median: 3
         Maximum: 6
         Mean: 3.476
         Std Deviation: 1.7083156448216303
        Distribution of 10000 Samples:
         1: 16.54%
         2: 17.11%
         3: 16.54%
         4: 16.56%
         5: 17.0%
         6: 16.25%
        
        Output Analysis: random_int(1, 6)
        Typical Timing: 63 ± 1 ns
        Statistics of 1000 Samples:
         Minimum: 1
         Median: 3
         Maximum: 6
         Mean: 3.515
         Std Deviation: 1.7363150030423022
        Distribution of 10000 Samples:
         1: 16.53%
         2: 16.88%
         3: 17.16%
         4: 16.64%
         5: 16.52%
         6: 16.27%
        
        Base Case
        Output Analysis: Random.randrange(6)
        Typical Timing: 813 ± 10 ns
        Statistics of 1000 Samples:
         Minimum: 0
         Median: 2
         Maximum: 5
         Mean: 2.494
         Std Deviation: 1.7195699813967797
        Distribution of 10000 Samples:
         0: 17.14%
         1: 16.5%
         2: 17.16%
         3: 16.13%
         4: 16.59%
         5: 16.48%
        
        Output Analysis: random_below(6)
        Typical Timing: 63 ± 1 ns
        Statistics of 1000 Samples:
         Minimum: 0
         Median: 3
         Maximum: 5
         Mean: 2.559
         Std Deviation: 1.7371057931330884
        Distribution of 10000 Samples:
         0: 16.39%
         1: 16.79%
         2: 16.69%
         3: 16.73%
         4: 16.21%
         5: 17.19%
        
        Output Analysis: binomial(4, 0.5)
        Typical Timing: 157 ± 7 ns
        Statistics of 1000 Samples:
         Minimum: 0
         Median: 2
         Maximum: 4
         Mean: 2.023
         Std Deviation: 1.0185816160271637
        Distribution of 10000 Samples:
         0: 6.25%
         1: 24.33%
         2: 37.79%
         3: 25.19%
         4: 6.44%
        
        Output Analysis: negative_binomial(5, 0.75)
        Typical Timing: 125 ± 3 ns
        Statistics of 1000 Samples:
         Minimum: 0
         Median: 1
         Maximum: 12
         Mean: 1.69
         Std Deviation: 1.5932477642973295
        Distribution of 10000 Samples:
         0: 23.56%
         1: 29.49%
         2: 22.08%
         3: 13.13%
         4: 6.8%
         5: 2.99%
         6: 1.23%
         7: 0.4%
         8: 0.24%
         9: 0.05%
         10: 0.02%
         12: 0.01%
        
        Output Analysis: geometric(0.75)
        Typical Timing: 63 ± 1 ns
        Statistics of 1000 Samples:
         Minimum: 0
         Median: 0
         Maximum: 5
         Mean: 0.35
         Std Deviation: 0.6985613702374245
        Distribution of 10000 Samples:
         0: 75.37%
         1: 18.49%
         2: 4.46%
         3: 1.15%
         4: 0.36%
         5: 0.12%
         6: 0.05%
        
        Output Analysis: poisson(4.5)
        Typical Timing: 94 ± 5 ns
        Statistics of 1000 Samples:
         Minimum: 0
         Median: 4
         Maximum: 12
         Mean: 4.501
         Std Deviation: 2.1171871626007657
        Distribution of 10000 Samples:
         0: 1.14%
         1: 4.56%
         2: 11.55%
         3: 17.14%
         4: 18.65%
         5: 16.61%
         6: 13.05%
         7: 8.39%
         8: 4.83%
         9: 2.37%
         10: 1.08%
         11: 0.39%
         12: 0.19%
         13: 0.03%
         14: 0.01%
         15: 0.01%
        
        
        Floating Point Tests
        
        Base Case
        Output Analysis: Random.random()
        Typical Timing: 32 ± 8 ns
        Statistics of 1000 Samples:
         Minimum: 8.071296181855203e-05
         Median: (0.502924505186587, 0.5032775656862833)
         Maximum: 0.9988170135684447
         Mean: 0.5016736438385434
         Std Deviation: 0.28790699393489927
        Post-processor Distribution of 10000 Samples using round method:
         0: 49.41%
         1: 50.59%
        
        Output Analysis: generate_canonical()
        Typical Timing: 32 ± 8 ns
        Statistics of 1000 Samples:
         Minimum: 0.0003245331885518794
         Median: (0.516565971390449, 0.5170143603852831)
         Maximum: 0.9990982713581824
         Mean: 0.5145717546453246
         Std Deviation: 0.2884522610970008
        Post-processor Distribution of 10000 Samples using round method:
         0: 49.92%
         1: 50.08%
        
        Output Analysis: random_float(0.0, 10.0)
        Typical Timing: 32 ± 8 ns
        Statistics of 1000 Samples:
         Minimum: 0.0236416469945347
         Median: (4.842279802871613, 4.848817506632073)
         Maximum: 9.987421014269959
         Mean: 4.927751085416743
         Std Deviation: 2.8771478669049926
        Post-processor Distribution of 10000 Samples using floor method:
         0: 10.6%
         1: 10.11%
         2: 10.21%
         3: 9.71%
         4: 9.99%
         5: 10.03%
         6: 9.74%
         7: 9.92%
         8: 9.87%
         9: 9.82%
        
        Base Case
        Output Analysis: Random.expovariate(1.0)
        Typical Timing: 313 ± 7 ns
        Statistics of 1000 Samples:
         Minimum: 0.0003062234170290055
         Median: (0.6996618370665845, 0.6997616759526261)
         Maximum: 7.159552971567411
         Mean: 1.0107104433923666
         Std Deviation: 0.9756097675287609
        Post-processor Distribution of 10000 Samples using floor_mod_10 method:
         0: 63.02%
         1: 23.79%
         2: 8.19%
         3: 3.35%
         4: 1.07%
         5: 0.28%
         6: 0.18%
         7: 0.07%
         8: 0.04%
         9: 0.01%
        
        Output Analysis: expovariate(1.0)
        Typical Timing: 63 ± 1 ns
        Statistics of 1000 Samples:
         Minimum: 0.0013738515219833946
         Median: (0.6659945404580686, 0.6679321003850357)
         Maximum: 6.184527605936165
         Mean: 0.9831307297176581
         Std Deviation: 0.9597396044531891
        Post-processor Distribution of 10000 Samples using floor_mod_10 method:
         0: 63.1%
         1: 23.67%
         2: 8.52%
         3: 3.09%
         4: 0.96%
         5: 0.4%
         6: 0.17%
         7: 0.06%
         8: 0.03%
        
        Base Case
        Output Analysis: Random.gammavariate(1.0, 1.0)
        Typical Timing: 469 ± 7 ns
        Statistics of 1000 Samples:
         Minimum: 0.0006982739611174922
         Median: (0.6926420900298924, 0.6938024016111628)
         Maximum: 6.103667741783101
         Mean: 0.9617465094971319
         Std Deviation: 0.925375520445067
        Post-processor Distribution of 10000 Samples using floor_mod_10 method:
         0: 63.19%
         1: 23.67%
         2: 8.08%
         3: 3.22%
         4: 1.21%
         5: 0.29%
         6: 0.18%
         7: 0.12%
         8: 0.03%
         9: 0.01%
        
        Output Analysis: gammavariate(1.0, 1.0)
        Typical Timing: 63 ± 4 ns
        Statistics of 1000 Samples:
         Minimum: 0.0008031925002940128
         Median: (0.760013069220213, 0.7620212798008952)
         Maximum: 8.144418057675459
         Mean: 1.0445620110501181
         Std Deviation: 1.0166196642405472
        Post-processor Distribution of 10000 Samples using floor_mod_10 method:
         0: 62.8%
         1: 23.32%
         2: 8.81%
         3: 3.09%
         4: 1.13%
         5: 0.59%
         6: 0.16%
         7: 0.07%
         8: 0.03%
        
        Base Case
        Output Analysis: Random.weibullvariate(1.0, 1.0)
        Typical Timing: 407 ± 8 ns
        Statistics of 1000 Samples:
         Minimum: 0.0033810095040254385
         Median: (0.7112308469171622, 0.7112703171484719)
         Maximum: 9.488506314169522
         Mean: 0.9923975403950522
         Std Deviation: 0.9769718629875843
        Post-processor Distribution of 10000 Samples using floor_mod_10 method:
         0: 63.45%
         1: 23.04%
         2: 8.38%
         3: 3.17%
         4: 1.12%
         5: 0.54%
         6: 0.22%
         7: 0.07%
         9: 0.01%
        
        Output Analysis: weibullvariate(1.0, 1.0)
        Typical Timing: 94 ± 7 ns
        Statistics of 1000 Samples:
         Minimum: 0.0001835254356867791
         Median: (0.6847916183943324, 0.685445483931424)
         Maximum: 7.664982901108874
         Mean: 0.9834642066050773
         Std Deviation: 0.9490050143193098
        Post-processor Distribution of 10000 Samples using floor_mod_10 method:
         0: 62.75%
         1: 23.34%
         2: 8.95%
         3: 3.1%
         4: 1.19%
         5: 0.41%
         6: 0.15%
         7: 0.08%
         8: 0.03%
        
        Output Analysis: extreme_value(0.0, 1.0)
        Typical Timing: 63 ± 8 ns
        Statistics of 1000 Samples:
         Minimum: -1.8690754939017922
         Median: (0.44928813309284127, 0.4495894525798417)
         Maximum: 8.274834739511434
         Mean: 0.6297141879838543
         Std Deviation: 1.3075096388384637
        Post-processor Distribution of 10000 Samples using round method:
         -3: 0.01%
         -2: 1.1%
         -1: 17.95%
         0: 34.93%
         1: 25.37%
         2: 12.63%
         3: 5.05%
         4: 1.77%
         5: 0.75%
         6: 0.29%
         7: 0.09%
         8: 0.04%
         9: 0.01%
         10: 0.01%
        
        Base Case
        Output Analysis: Random.gauss(5.0, 2.0)
        Typical Timing: 563 ± 8 ns
        Statistics of 1000 Samples:
         Minimum: -1.8217604282017907
         Median: (5.051563263666794, 5.056609353790892)
         Maximum: 11.366999578488945
         Mean: 5.022749094201779
         Std Deviation: 1.9888992063128028
        Post-processor Distribution of 10000 Samples using round method:
         -2: 0.06%
         -1: 0.27%
         0: 1.01%
         1: 2.71%
         2: 6.03%
         3: 12.23%
         4: 18.01%
         5: 20.12%
         6: 17.19%
         7: 12.14%
         8: 6.31%
         9: 2.82%
         10: 0.86%
         11: 0.22%
         12: 0.02%
        
        Output Analysis: normalvariate(5.0, 2.0)
        Typical Timing: 94 ± 1 ns
        Statistics of 1000 Samples:
         Minimum: -2.0429195708720473
         Median: (4.901235259005797, 4.907550179190664)
         Maximum: 10.669609948445181
         Mean: 4.94351243779702
         Std Deviation: 1.9964278474382608
        Post-processor Distribution of 10000 Samples using round method:
         -2: 0.04%
         -1: 0.21%
         0: 0.92%
         1: 2.79%
         2: 6.39%
         3: 11.72%
         4: 17.37%
         5: 20.17%
         6: 17.63%
         7: 12.32%
         8: 6.33%
         9: 2.94%
         10: 0.92%
         11: 0.21%
         12: 0.04%
        
        Base Case
        Output Analysis: Random.lognormvariate(1.6, 0.25)
        Typical Timing: 813 ± 20 ns
        Statistics of 1000 Samples:
         Minimum: 2.4909789931331665
         Median: (4.9469536651658785, 4.948132695702176)
         Maximum: 13.298525089985707
         Mean: 5.087805333829117
         Std Deviation: 1.2981416549832467
        Post-processor Distribution of 10000 Samples using round method:
         2: 0.31%
         3: 8.0%
         4: 27.38%
         5: 30.97%
         6: 19.58%
         7: 9.05%
         8: 3.15%
         9: 1.1%
         10: 0.32%
         11: 0.09%
         12: 0.01%
         13: 0.04%
        
        Output Analysis: lognormvariate(1.6, 0.25)
        Typical Timing: 94 ± 7 ns
        Statistics of 1000 Samples:
         Minimum: 2.4428465738204808
         Median: (4.9702179719562, 4.986770005889918)
         Maximum: 10.899243194201839
         Mean: 5.138117941614769
         Std Deviation: 1.2772123749368882
        Post-processor Distribution of 10000 Samples using round method:
         2: 0.33%
         3: 8.03%
         4: 26.61%
         5: 31.9%
         6: 19.59%
         7: 8.79%
         8: 3.13%
         9: 1.21%
         10: 0.31%
         11: 0.09%
         13: 0.01%
        
        Output Analysis: chi_squared(1.0)
        Typical Timing: 125 ± 6 ns
        Statistics of 1000 Samples:
         Minimum: 4.844684729641287e-08
         Median: (0.4759465801667526, 0.4795442615373534)
         Maximum: 11.409981115784303
         Mean: 0.9842604379661425
         Std Deviation: 1.3307195042368343
        Post-processor Distribution of 10000 Samples using floor_mod_10 method:
         0: 67.52%
         1: 16.61%
         2: 7.66%
         3: 3.63%
         4: 2.25%
         5: 1.13%
         6: 0.63%
         7: 0.33%
         8: 0.12%
         9: 0.12%
        
        Output Analysis: cauchy(0.0, 1.0)
        Typical Timing: 63 ± 8 ns
        Statistics of 1000 Samples:
         Minimum: -1972.6916185138157
         Median: (0.011961584087712716, 0.025839640114348472)
         Maximum: 759.9098882259025
         Mean: -2.3953059757615565
         Std Deviation: 69.10559085334823
        Post-processor Distribution of 10000 Samples using floor_mod_10 method:
         0: 26.19%
         1: 11.39%
         2: 5.78%
         3: 4.13%
         4: 2.97%
         5: 3.27%
         6: 3.63%
         7: 5.82%
         8: 11.71%
         9: 25.11%
        
        Output Analysis: fisher_f(8.0, 8.0)
        Typical Timing: 188 ± 8 ns
        Statistics of 1000 Samples:
         Minimum: 0.11290449448637255
         Median: (0.9539784476590794, 0.9545294808989871)
         Maximum: 15.141314152453434
         Mean: 1.296483358429577
         Std Deviation: 1.1955157250860309
        Post-processor Distribution of 10000 Samples using floor_mod_10 method:
         0: 50.5%
         1: 32.68%
         2: 10.04%
         3: 3.43%
         4: 1.79%
         5: 0.77%
         6: 0.42%
         7: 0.22%
         8: 0.09%
         9: 0.06%
        
        Output Analysis: student_t(8.0)
        Typical Timing: 157 ± 8 ns
        Statistics of 1000 Samples:
         Minimum: -4.090486444127238
         Median: (-0.07053637383717018, -0.069951340813996)
         Maximum: 4.475852462387794
         Mean: -0.056553151681631296
         Std Deviation: 1.127945410932898
        Post-processor Distribution of 10000 Samples using round method:
         -7: 0.02%
         -6: 0.02%
         -5: 0.05%
         -4: 0.31%
         -3: 1.36%
         -2: 6.6%
         -1: 23.17%
         0: 36.64%
         1: 23.39%
         2: 6.57%
         3: 1.48%
         4: 0.31%
         5: 0.05%
         6: 0.02%
         7: 0.01%
        
        
        =========================================================================
        Total Test Time: 0.6052 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
