Metadata-Version: 2.2
Name: pyCLINE
Version: 0.1.7
Summary: This package is the python implementation of the CLINE method introduced by Prokop, Billen, Frolov, Gelens (2025).
Author-email: Bartosz Prokop <bartosz.prokop@kuleuven.be>, Nikita Frolov <nikita.frolov@kuleuven.be>, Lendert Gelens <lendert.gelens@kuleuven.be>
Project-URL: Homepage, https://pycline-ec8369.pages.gitlab.kuleuven.be/
Project-URL: Issues, https://gitlab.kuleuven.be/gelenslab/publications/pycline/-/issues
Keywords: model,model identification,nullcline,data-driven,machine learning,deep learning,torch,dynamics,oscillator,nonlinear dynamics,complex systems
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: matplotlib>=3.6.2
Requires-Dist: numpy<1.25.0,>=1.24.1
Requires-Dist: pandas~=1.5.2
Requires-Dist: torch>=2.4.1
Requires-Dist: tqdm>=4.66.1
Requires-Dist: jitcdde>=1.8.1
Requires-Dist: scipy>=1.9.3

# PyCLINE - python package for CLINE

The `pyCLINE` package is the python package based on the CLINE (**C**omputational **L**earning and **I**dentification of **N**ullclin**E**s).
It can be downloaded from PyPI with pip by using
    
    pip install pyCLINE

The package allows to recreate all data, models and results shown in XXX, and to apply CLINE to other data sets. 
In order to generate data used in XXX, a set of different models is being provided under `pyCLINE.model`. 
Data from these models can be generated using `pyCLINE.generate_data()`.
For setting up the data prepartion and adjacent training a neural network, the submodule `pyCLINE.recovery_methods` is used. 
The submodule contains the module for data_preparation `pyCLINE.recovery_methods.data_preparation` and for neural network training `pyCLINE.recovery_methods.nn_training`. 

For a better understanding, `pyCLINE` also contains the module `pyCLINE.example` which provides four example also found in XXX with step by step instructions on how to setup a CLINE pipeline. 

The structure of `pyCLINE` is shown here: 

![PyCLINE structure](pycline_structure.png)
