📦 Python library for Stochastic Processes Simulation and Visualisation
The aleatory (/ˈeɪliətəri/) Python library provides functionality for simulating and visualising stochastic processes. More precisely, it introduces objects representing a number of continuous-time stochastic processes $X = (X_t : t\geq 0)$ and provides methods to:
Currently, aleatory
supports the following processes:
Aleatory is available on pypi and can be installed as follows
pip install aleatory
Aleatory relies heavily on
numpy
for random number generationscipy
and statsmodels
for support for a number of one-dimensional distributions.matplotlib
for creating visualisationsAleatory is tested on Python versions 3.8, 3.9, 3.10, and 3.11
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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