MonteCarlo simulation algorithm

Requirements

Python 3.10.0+ Currently working to introduce support to old version.

Installation

pip install montecarlosim

How to use it

Import the Montecarlo class, create an instance of it by passing a function that accepts a one-dimensional numpy array and return a numpy array with the same shape (you can see them as passing the X axis points and returning the relative Y axis points).
If you don't have a function to use, but you want to test the library, you can use a gaussian function inside the functions.py file.
With the newly created instance, call one of the Montecarlo simulation methods (hit_or_miss or average) and pass them: min value of X axis, max value of Y axis and number of samples. This method will return the results (dictionary), hit_or_miss dict contains hit points and miss points also.
If you want to graphically see your results, you can use the plot static method of Montecarlo's class by passing it the reuslts dict of every simulation method.
Example:

from montecarlosim.montecarlo import Montecarlo
from montecarlosim.functions import gauss

simulation = Montecarlo(gauss)
results = simulation.hit_or_miss(-3, 3, 1000)
Montecarlo.plot(results)

More on Montecarlo class:
You can also decide which criteria to use for defining how to read HIT or MISS points (will affect hit_or_miss method only). These criteria are defined inside the Criteria enum class inside CriteriaValue class inside Montecarlo class.

from montecarlosim.montecarlo import Montecarlo

for criteria in Montecarlo.CriteriaValue.Criteria:
    print(criteria.value)

# By being an ENUM you can access a single criteria like that
print(Montecarlo.CriteriaValue.Criteria.MINOR.value)

Getting started

• Clone the repository the way you like;
• Create a virtualenv (with pyenv and virtualenv you can run pyenv virtualenv 3.10.0 <name_you_like>) otherwise install poetry and run poetry install, it will take care of the virtualenv itself;
• Now you can lunch the tests with poetry run tests and it will tests all environment inside tox.ini envlist;
• Create a branch, do your changes, push them and open a PR.

Repository structure

Repository configuration files are:

• pyproject.toml;
• tox.ini, tests;
• src, contains the actual repository code;
• src/montecarlo.py, contains the Montecarlo class (where magic happens);
• src/functions.py, contains example functions to be used with Montecarlo class;
• src/exceptions.py, contains custom exception that are used to better handle errors;
• tests, contains python tests file that is launched by tox;
• run_tests.py, simple function that run subprocess.run('tox').