Xiang Qu, Hui Zhao, Wenjie Cai, Gongyi Wang, and Zihan Huang
School of Physics and Electronics, Hunan University, Changsha 410082, China
E-mail: huangzih@hnu.edu.cn
GenML is a Python library designed for generating Mittag-Leffler correlated noise, which is crucial for modeling a wide range of phenomena in complex systems. This document provides a brief overview of how to install and use GenML to generate M-L noise and compute its autocorrelation functions.
Documentation website: https://genml.readthedocs.io
To install GenML, simply run the following command in your Python environment:
pip install -U genml
The core functionalities of GenML include generating sequences of Mittag-Leffler correlated noise and calculating their autocorrelation functions. Here's how you can get started:
To generate sequences of Mittag-Leffler correlated noise , use the mln
function with the desired parameters:
import genml
# Parameters
N = 10 # Number of sequences
T = 500 # Length of each sequence
C = 1.0 # Amplitude coefficient
lamda = 0.5 # Mittag-Leffler exponent
tau = 10 # Characteristic memory time
seed = 42 # Random seed
# Generate M-L noise sequences
xi = genml.mln(N, T, C, lamda, tau, seed)
To calculate the autocorrelation function (ACF) values of the generated noise sequences, you can use the acf
function for actual ACF values and the acft
function for theoretical ACF values:
tmax = 100 # Max lag for ACF calculation
dt = 1 # Step size between lags
nc = 4 # Number of CPU cores for parallel processing
# Calculate actual ACF values
acfv = genml.acf(xi, tmax, dt, nc)
# Calculate theoretical ACF values
acftv = genml.acft(tmax, dt, C, lamda, tau)
The repository includes detailed examples illustrating the generation of Mittag-Leffler correlated noise and the calculation of its autocorrelation function. These examples demonstrate the library's capability to replicate theoretical noise properties.