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.
- Website: https://sysidentpy.org
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 | ok |
| macOS | 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.
Note: we use the pytest package for testing. The test functions are located in tests subdirectories at each folder inside SysIdentPy, which check the validity of the algorithms.
Run the pytest in the respective folder to perform all the tests of the corresponding sub-packages.
Currently, we have around 81% of code coverage.
You can install pytest using
pip install -U pytest
Open a terminal emulator of your choice and go to a subdirectory, e.g,
\sysidentpy\metrics\
Just type pytest and you get a result like
========== test session starts ==========
platform linux -- Python 3.7.6, pytest-5.4.2, py-1.8.1, pluggy-0.13.1
rootdir: ~/sysidentpy
plugins: cov-2.8.1
collected 12 items
tests/test_regression.py ............ [100%]
========== 12 passed in 2.45s ==================
You can also see the code coverage using the pytest-cov package. First, install pytest-cov using
pip install pytest-cov
Run the command below in the SysIdentPy root directory, to generate the report.
pytest --cov=.
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Official source code repo: https://github.com/wilsonrljr/sysidentpy
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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.
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Discord server: https://discord.gg/8eGE3PQ
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Website: http://sysidentpy.org
If you use SysIdentPy on your project, please drop me a line.
If you use SysIdentPy on your scientific publication, we would appreciate citations to the following paper:
- Lacerda et al., (2020). SysIdentPy: A Python package for System Identification using NARMAX models. Journal of Open Source Software, 5(54), 2384, https://doi.org/10.21105/joss.02384
@article{Lacerda2020,
doi = {10.21105/joss.02384},
url = {https://doi.org/10.21105/joss.02384},
year = {2020},
publisher = {The Open Journal},
volume = {5},
number = {54},
pages = {2384},
author = {Wilson Rocha Lacerda Junior and Luan Pascoal Costa da Andrade and Samuel Carlos Pessoa Oliveira and Samir Angelo Milani Martins},
title = {SysIdentPy: A Python package for System Identification using NARMAX models},
journal = {Journal of Open Source Software}
}
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.