Metadata-Version: 2.1
Name: genml
Version: 0.3.1
Summary: A Python package for generating Mittag-Leffler correlated noise
Home-page: UNKNOWN
License: UNKNOWN
Description: # GenML: A Python Library to Generate the Mittag-Leffler Correlated Noise
        
        > Xiang Qu, Hui Zhao, Wenjie Cai, Gongyi Wang, and [Zihan Huang](https://grzy.hnu.edu.cn/site/index/huangzihan)
        
        *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.
        
        ## Installation
        
        To install GenML, simply run the following command in your Python environment:
        
        ```bash
        pip install -U genml
        ```
        
        ## Basic Usage
        
        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:
        
        ### Generating Mittag-Leffler Correlated Noise
        
        To generate sequences of Mittag-Leffler correlated noise , use the `mln` function with the desired parameters:
        
        ```python
        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
        nc = 4  # Number of CPU cores for parallel processing
        
        # Generate M-L noise sequences
        xi = genml.mln(N, T, C, lamda, tau, nc)
        ```
        
        ### Calculating Autocorrelation Function
        
        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:
        
        ```python
        tmax = 100  # Max lag for ACF calculation
        dt = 1  # Step size between lags
        
        # Calculate actual ACF values
        acfv = genml.acf(xi, tmax, dt, nc)
        
        # Calculate theoretical ACF values
        acftv = genml.acft(tmax, dt, C, lamda, tau)
        ```
        
        ### Examples
        
        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.
        
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
