RiskSimp 0.1.5

RiskSimp is a versatile Python library designed to streamline the management and manipulation of random variables to empower analysts to effortlessly integrate random distributions into their processes, facilitating non-deterministic analysis

RiskSimp: Simplifying Random Variables and Simulation in Python

Version: 0.1.5

RiskSimp is a versatile Python library designed to streamline the management and manipulation of random variables, empowering analysts to effortlessly integrate random distributions into their processes and facilitate non-deterministic analysis.

Table of Contents

• Installation
• Usage
• Modules
  • Objects
  • Utils
  • Variables
    • Continuous
    • Discrete
    • Constant
• Example
• License

Installation {#installation}

You can install RiskSimp using pip:

pip install RiskSimp

Usage {#usage}

Import the required modules and classes from RiskSimp:

from RiskSimp import Continuous, Discrete, Constant, Simulation

Modules {#modules}

Objects {#objects}

In the "objects" module, you'll find the base classes used throughout the library.

Utils {#utils}

The "utils" module contains classes to simulate processes with random variables.

Variables {#variables}

The "variables" module houses various classes for different types of random variables.

Continuous {#continuous}

• Uniform(cls, a: Union[int, float], b: Union[int, float])
• Triangular(cls, min_val: Union[int, float], mode: Union[int, float], max_val: Union[int, float])
• Normal(cls, mean: Union[int, float], std_dev: Union[int, float])
• Exponential(cls, scale: Union[int, float])
• Beta(cls, alpha: Union[int, float], beta: Union[int, float])
• Gamma(cls, shape: Union[int, float], scale: Union[int, float])
• Weibull(cls, alpha: Union[int, float], beta: Union[int, float])
• LogNormal(cls, mean: Union[int, float], std_dev: Union[int, float])

Discrete {#discrete}

• Poisson(cls, lam: Union[int, float])
• Uniform(cls, a: int, b: int)
• Triangular(cls, min_val: int, mode: int, max_val: int)
• Binomial(cls, n: int, p: float)
• Bernoulli(cls, p: float, v_occurrence: Union[int, float] = 1, v_non_occurrence: Union[int, float] = 0)

Constant {#constant}

• Constant(subclass of Distribution): Represents a non-random constant value.

Example {#example}

from RiskSimp import *
import numpy_financial as npf


def restaurante(adecuacion,
                costo_fijo,
                costo_variable,
                inflacion,
                precio_cerveza,
                precio_alitas,
                afluencia_anual,
                variacion_demanda,
                tasa_de_comparacion,
                tasa_descuento):
    years = 11
    adec = [0] * years
    adec[0] = adecuacion
    c_fijo = [costo_fijo * (1 + inflacion) ** i for i in range(years)]
    demanda = [0] + [round(afluencia_anual * (1 + variacion_demanda) ** i) for i in range(years - 1)]
    p_cerveza = [0] + [precio_cerveza * (1 + inflacion) ** i for i in range(years - 1)]
    p_alitas = [0] + [precio_alitas * (1 + inflacion) ** i for i in range(years - 1)]
    ingreso_cerveza = [p * d for p, d in zip(p_cerveza, demanda)]
    ingreso_alitas = [p * d for p, d in zip(p_alitas, demanda)]
    ingreso = [c + a for c, a in zip(ingreso_cerveza, ingreso_alitas)]
    c_variable = [i * costo_variable for i in ingreso]

    flujo = [ing - fij - var - inv for ing, fij, var, inv in zip(ingreso, c_fijo, c_variable, adec)]
    return npf.npv(tasa_descuento, [0] + flujo), npf.irr(flujo)



adecuacion = Constant(1_500_000_000)
adecuacion.change_name("adecuacion")
costo_fijo = Constant(5_000_000)
costo_fijo.change_name("costo_fijo")
costo_variable = Continuous.Uniform(.01, .04)
costo_variable.change_name("costo_variable")
inflacion = Continuous.Normal(.047, 0.002)
inflacion.change_name("inflacion")
precio_cerveza = Continuous.Triangular(12_000, 13_500, 15_000)
precio_cerveza.change_name("precio_cerveza")
precio_alitas = Continuous.Uniform(25_000, 30_000)
precio_alitas.change_name("precio_alitas")
afluencia_anual = Continuous.Triangular(4000, 6000, 8000)
afluencia_anual.change_name("afluencia_anual")
variacion_demanda = Continuous.Uniform(0.02, 0.04)
variacion_demanda.change_name("variacion_demanda")
tasa_de_comparacion = Constant(0.14)
tasa_de_comparacion.change_name("tasa_de_comparacion")
tasa_descuento = Constant(0.1522)
tasa_descuento.change_name("tasa_descuento")

sim = Simulation(restaurante)
sim.set_inputs(adecuacion,
               costo_fijo,
               costo_variable,
               inflacion,
               precio_cerveza,
               precio_alitas,
               afluencia_anual,
               variacion_demanda,
               tasa_de_comparacion,
               tasa_descuento)

sim.complete_analisys()

Output:

• histogram for output 1

• histogram for output 2

• spider plot for output 1

• spider plot for output 2

• inputs table for output 1

• outputs table for output 1

• Tornado chart for output 1

• inputs table for output 2

• outputs table for output 2

• Tornado chart for output 2

• range of 90% confidence

• lower tail 90% confidence

• upper tail 90% confidence

License

This project is licensed under the MIT License - see the LICENSE file for details.