sysidentpy is a Python module for System Identification using NARMAX models built on top of numpy and is distributed under the 3-Clause BSD license.
The project was started in by Wilson R. L. Junior, Luan Pascoal C. Andrade and Samir A. M. Martins as a project for System Identification discipline. Samuel joined early in 2019 and since then have contributed.
The examples directory has several Jupyter notebooks presenting basic tutorials of how to use the package and some specific applications of sysidentpy. Try it out!
sysidentpy requires:
- Python (>= 3.6)
- NumPy (>= 1.5.0) for all numerical algorithms
- Matplotlib >= 1.5.2 for static plotiing and visualizations
| Platform | Status |
|---|---|
| Linux | OK |
| Windows x64 | OK |
SysIdentPy do not to support Python 2.7.
A few examples require pandas >= 0.18.0. However, it is not required to use sysidentpy.
The easiest way to get sysidentpy running is to install it using pip
pip install sysidentpy
We will made it available at conda repository as soon as possible.
See the changelog for a history of notable changes to sysidentpy.
We welcome new contributors of all experience levels. The sysidentpy community goals are to be helpful, welcoming, and effective.
- Official source code repo: https://github.com/wilsonrljr/sysidentpy
- Download releases: https://pypi.org/project/sysidentpy/
You can check the latest sources with the command:
git clone https://github.com/wilsonrljr/sysidentpy.git
The project was started by Wilson R. L. Junior, Luan Pascoal and Samir A. M. Martins as a project for System Identification discipline. Samuel joined early in 2019 and since then have contributed.
The initial purpose was to learn the python language. Over time, the project has matured to the state it is in today.
The project is currently maintained by its creators and looking for contributors.
- Discord server: https://discord.gg/8eGE3PQ
- Website(soon): http://sysidentpy.org
More information coming soon.
The documentation and structure (even this section) is openly inspired by sklearn, einsteinpy, and many others as we used (and keep using) them to learn.