aleatory
Overview
The aleatory (/ˈeɪliətəri/) Python library provides functionality for simulating and visualising stochastic processes. More precisely, it introduces objects representing a number of stochastic processes and provides methods to:
- generate realizations/trajectories from each process —over discrete time sets
- create visualisations to illustrate the processes properties and behaviour
Currently, aleatory supports the following stochastic processes in one dimension:
- Arithmetic Brownian Motion (see Brownian Motion)
- Bessel process
- Brownian Bridge
- Brownian Excursion
- Brownian Meander
- Brownian Motion
- Constant Elasticity Variance (CEV) process
- Cox–Ingersoll–Ross (CIR) process
- Chan-Karolyi-Longstaff-Sanders (CKLS) process
- Fractional Brownian Motion process
- Galton-Watson process with Poisson branching
- Gamma process
- Gaussian Process with Constant Kernel
- Gaussian Process with Linear Kernel
- Gaussian Process with Matern Kernel
- Gaussian Process with Periodic Kernel
- Gaussian Process with RBF Kernel
- Gaussian Process with Squared Exponential Kernel
- General Random Walk
- Geometric Brownian Motion
- Hawkes process
- Inverse Gaussian process
- Inhomogeneous Poisson process
- Mixed Poisson process
- Ornstein–Uhlenbeck (OU) process
- Poisson process
- Random Walk
- Squared Bessel processes
- Vasicek process
- Variance-Gamma process
- White Noise
From v1.1.1 aleatory supports the following 2-d stochastic processes:
Installation
Aleatory is available on pypi and can be installed as follows
pip install aleatory
Dependencies
Aleatory relies heavily on
numpyfor random number generationscipyandstatsmodelsfor support for a number of one-dimensional distributions.matplotlibfor creating visualisations
Compatibility
Aleatory is tested on Python versions 3.8, 3.9, 3.10, and 3.11
Quick-Start
Aleatory allows you to create fancy visualisations from different stochastic processes in an easy and concise way.
For example, the following code
from aleatory.processes import BrownianMotion
brownian = BrownianMotion()
brownian.draw(n=100, N=100, colormap="cool", figsize=(12,9))
generates a chart like this:
For more examples visit the Quick-Start Guide.
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