Distribution Tests & Performance Timer for Non-deterministic Functions

• Fortuna: Collection of abstractions to make custom random generators. https://pypi.org/project/Fortuna/

• Python 3.7 or later with dev tools (setuptools, pip, etc.)
• Cython: Bridge from C/C++ to Python.
• Modern C++ compiler and Standard Library. Clang or GCC.

• MonkeyScope.distribution_timer(func: staticmethod, *args, **kwargs) -> None
  • Logger for the statistical analysis of non-deterministic generators.
  • @param func :: function, method or lambda to analyze. Evaluated as func(*args, **kwargs)
  • @optional_kw num_cycles=10000 :: Total number of samples to use for analysis.
  • @optional_kw post_processor=None staticmethod :: Used to scale a large set of data into a smaller set of groupings for better visualization of the data, esp. useful for distributions of floats. For many functions in quick_test(), math.floor() is used, for others round() is more appropriate. For more complex post processing - lambdas work nicely. Post processing only affects the distribution, the statistics and performance results are unaffected.
• MonkeyScope.distribution(func: staticmethod, *args, **kwargs) -> None
  • Stats and distribution.
• MonkeyScope.timer(func: staticmethod, *args, **kwargs) -> None
  • Just the function timer.

import MonkeyScope, random


x, y, z = 1, 10, 2
MonkeyScope.distribution_timer(random.randint, x, y)
MonkeyScope.distribution_timer(random.randrange, x, y)
MonkeyScope.distribution_timer(random.randrange, x, y, z)

Output Analysis: Random.randint(1, 10)
Typical Timing: 1270 ± 88 ns
Statistics of 1000 samples:
 Minimum: 1
 Median: 5.0
 Maximum: 10
 Mean: 5.425
 Std Deviation: 2.867468395641005
Distribution of 100000 samples:
 1: 10.0%
 2: 10.073%
 3: 10.046%
 4: 10.07%
 5: 10.032%
 6: 9.991%
 7: 9.978%
 8: 10.115%
 9: 9.836%
 10: 9.859%

Output Analysis: Random.randrange(1, 10)
Typical Timing: 1135 ± 69 ns
Statistics of 1000 samples:
 Minimum: 1
 Median: 5.0
 Maximum: 9
 Mean: 5.028
 Std Deviation: 2.605228588819031
Distribution of 100000 samples:
 1: 11.281%
 2: 11.098%
 3: 11.04%
 4: 11.119%
 5: 10.999%
 6: 11.176%
 7: 11.206%
 8: 11.091%
 9: 10.99%

Output Analysis: Random.randrange(1, 10, 2)
Typical Timing: 1332 ± 55 ns
Statistics of 1000 samples:
 Minimum: 1
 Median: 5.0
 Maximum: 9
 Mean: 5.068
 Std Deviation: 2.771890329720857
Distribution of 100000 samples:
 1: 19.868%
 3: 20.025%
 5: 20.001%
 7: 19.811%
 9: 20.295%

• Resolves bug caused by attempting to generate a distribution of non-numeric values.

• Updates calling signature of distribution and distribution_timer

• Adds toml file to aid installation

• Documentation Update

• MonkeyScope no longer requires C++17 compiler. Any C++ compiler should work.

• Documentation Update
• Nano second precision enabled with time_ns

• No longer requires numpy
• Requires Python3.7 or later

• Internal Performance Update
• Final 3.6 release

• Docs update

• Installer Update, will properly install numpy as needed.

• Fixed Typos

• More minor typos fixed

• Minor typos fixed.

• MonkeyScope is now compatible with python notebooks.

• Documentation update

• Minor performance improvement.

• Public Release

• Installer Update

• Minor Bug Fix

• Continued Development

• Renamed to MonkeyScope

• Changed to c++ compiler

• Initial Project Setup