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:
- generate realizations/trajectories from each process —over discrete time sets
- create visualisations to illustrate the processes properties and behaviour
aleatory supports the following processes:
- Brownian Motion
- Geometric Brownian Motion
- Constant Elasticity
- Bessel Process
- Squared Bessel Processs
Aleatory is available on pypi and can be installed as follows
pip install aleatory
Aleatory relies heavily on
numpyfor random number generation
statsmodelsfor support for a number of one-dimensional distributions.
matplotlibfor creating visualisations
Aleatory is tested on Python versions 3.8, 3.9, and 3.10
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 example visit the Quick-Start Guide.
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