DOC: Add academic use guide, remove dupe content from README.rst

Also add citation.cff

Author: Jacob-Stevens-Haas

README.rst

@@ -3,13 +3,16 @@ PySINDy
 
 |BuildCI| |RTD| |PyPI| |Codecov| |JOSS1| |JOSS2| |DOI|
 
-**PySINDy** is a sparse regression package with several implementations for the Sparse Identification of Nonlinear Dynamical systems (SINDy) method introduced in Brunton et al. (2016a), including the unified optimization approach of Champion et al. (2019), SINDy with control from Brunton et al. (2016b), Trapping SINDy from Kaptanoglu et al. (2021), SINDy-PI from Kaheman et al. (2020), PDE-FIND from Rudy et al. (2017), and so on. A comprehensive literature review is given in de Silva et al. (2020) and Kaptanoglu, de Silva et al. (2021).
-
-.. contents:: Table of contents
+**PySINDy** is a sparse regression package with several implementations for the
+Sparse Identification of Nonlinear Dynamical systems (SINDy) method introduced
+in Brunton et al. (2016a).
+It also includes a variety of other methods from related literature.
+A comprehensive literature review is given in de Silva et al. (2020) and Kaptanoglu, de Silva et al. (2021).
 
 System identification
 ---------------------
-System identification refers to the process of leveraging measurement data to infer governing equations, in the form of dynamical systems, describing the data. Once discovered, these equations can make predictions about future states, can inform control inputs, or can enable the theoretical study using analytical techniques.
+System identification refers to the process of leveraging measurement data to infer governing equations, in the form of dynamical systems, describing the data.
+Once discovered, these equations can make predictions about future states, can inform control inputs, or can enable the theoretical study using analytical techniques.
 Dynamical systems are a flexible, well-studied class of mathematical objects for modeling systems evolving in time.
 SINDy is a model discovery method which uses *sparse regression* to infer nonlinear dynamical systems from measurement data.
 The resulting models are inherently *interpretable* and *generalizable*.
@@ -48,7 +51,8 @@ The PySINDy package revolves around the ``SINDy`` class which consists of three
 * ``feature_library``: specifies the candidate basis functions to be used to construct ``Theta(X)``
 * ``optimizer``: implements a sparse regression method for solving for ``Xi``
 
-Once a ``SINDy`` object has been created it must be fit to measurement data, similar to a ``scikit-learn`` model. It can then be used to predict derivatives given new measurements, evolve novel initial conditions forward in time, and more. PySINDy has been written to be as compatible with ``scikit-learn`` objects and methods as possible.
+Once a ``SINDy`` object has been created it must be fit to measurement data, similar to a ``scikit-learn`` model.
+It can then be used to predict derivatives given new measurements, evolve novel initial conditions forward in time, and more.
 
 Example
 ^^^^^^^
@@ -98,9 +102,8 @@ PySINDy provides numerous other features not shown here. We recommend the `featu
 Installation
 ------------
 
-The preferred way to install is with pip or conda e.g. ``pip install pysindy``
-
-You may have to add ``--user`` option to the above lines.
+The preferred way to install is with pip or conda e.g. ``pip install pysindy``.
+You may have to add ``--user`` option.
 Pysindy also provides several extras:
 
 cvxpy
@@ -115,114 +118,28 @@ sbr
 
 Documentation
 -------------
-The documentation site for PySINDy can be found `here <https://pysindy.readthedocs.io/en/latest/>`__. There are numerous `examples <https://pysindy.readthedocs.io/en/latest/examples/index.html>`_ of PySINDy in action to help you get started. Examples are also available as `Jupyter notebooks <https://github.com/dynamicslab/pysindy/tree/master/examples>`__. A video overview of PySINDy can be found on `Youtube <https://www.youtube.com/watch?v=DvbbXX8Bd90>`__. We have also created a `video playlist <https://www.youtube.com/playlist?list=PLN90bHJU-JLoOfEk0KyBs2qLTV7OkMZ25>`__ with practical PySINDy tips.
+The documentation site for PySINDy can be found `here <https://pysindy.readthedocs.io/en/latest/>`__.
+There are numerous `examples <https://pysindy.readthedocs.io/en/latest/examples/index.html>`_ of PySINDy in action to help you get started.
+Examples are also available as `Jupyter notebooks <https://github.com/dynamicslab/pysindy/tree/master/examples>`__.
+A video overview of PySINDy can be found on `Youtube <https://www.youtube.com/watch?v=DvbbXX8Bd90>`__.
+We have also created a `video playlist <https://www.youtube.com/playlist?list=PLN90bHJU-JLoOfEk0KyBs2qLTV7OkMZ25>`__ with practical PySINDy tips.
+
+If something is unclear, please open an issue.  To discuss your particular dynamics problem, open a discussion.  Make sure to format your example as python code in github!
 
 PySINDy implements a lot of advanced functionality that may be overwhelming for new users or folks who are unfamiliar with these methods. Below (see here if image does not render https://github.com/dynamicslab/pysindy/blob/master/docs/JOSS2/Fig3.png), we provide a helpful flowchart for figuring out which methods to use, given the characteristics of your dataset:
 
 .. image:: https://github.com/dynamicslab/pysindy/blob/master/docs/JOSS2/Fig3.png
 
 This flow chart summarizes how ``PySINDy`` users can start with a dataset and systematically choose the proper candidate library and sparse regression optimizer that are tailored for a specific scientific task. The ``GeneralizedLibrary`` class allows for tensoring, concatenating, and otherwise combining many different candidate libraries.
 
-Community guidelines
---------------------
-
-Contributing
-^^^^^^^^^^^^^^^^^^^^^
-See `Contributor guide <https://pysindy.readthedocs.io/en/latest/contributing.html>`_
-
-Reporting issues or bugs
-^^^^^^^^^^^^^^^^^^^^^^^^
-If you find a bug in the code or want to request a new feature, please open an issue.
+Contributions:
+-----------------------------
+See `Contributor guide <https://pysindy.readthedocs.io/en/latest/contributing.html>`_.
 
-Getting help
-^^^^^^^^^^^^
-For help using PySINDy please consult the `documentation <https://pysindy.readthedocs.io/en/latest/>`__ and/or our `examples <https://github.com/dynamicslab/pysindy/tree/master/examples>`__, or create an issue.
 
 Citing PySINDy
---------------
-PySINDy has been published in the Journal of Open Source Software (JOSS). The paper can be found `here <https://joss.theoj.org/papers/10.21105/joss.02104>`__.
-
-If you use PySINDy in your work, please cite it using the following two references:
-
-Brian M. de Silva, Kathleen Champion, Markus Quade, Jean-Christophe Loiseau, J. Nathan Kutz, and Steven L. Brunton., (2020). *PySINDy: A Python package for the sparse identification of nonlinear dynamical systems from data.* Journal of Open Source Software, 5(49), 2104, https://doi.org/10.21105/joss.02104
-
-Kaptanoglu et al., (2022). PySINDy: A comprehensive Python package for robust sparse system identification. Journal of Open Source Software, 7(69), 3994, https://doi.org/10.21105/joss.03994
-
-Bibtex:
-
-.. code-block:: text
-
-    @article{desilva2020,
-    doi = {10.21105/joss.02104},
-    url = {https://doi.org/10.21105/joss.02104},
-    year = {2020},
-    publisher = {The Open Journal},
-    volume = {5},
-    number = {49},
-    pages = {2104},
-    author = {Brian de Silva and Kathleen Champion and Markus Quade and Jean-Christophe Loiseau and J. Kutz and Steven Brunton},
-    title = {PySINDy: A Python package for the sparse identification of nonlinear dynamical systems from data},
-    journal = {Journal of Open Source Software}
-    }
-
-Bibtex:
-
-.. code-block:: text
-
-    @article{Kaptanoglu2022,
-    doi = {10.21105/joss.03994},
-    url = {https://doi.org/10.21105/joss.03994},
-    year = {2022},
-    publisher = {The Open Journal},
-    volume = {7},
-    number = {69},
-    pages = {3994},
-    author = {Alan A. Kaptanoglu and Brian M. de Silva and Urban Fasel and Kadierdan Kaheman and Andy J. Goldschmidt and Jared Callaham and Charles B. Delahunt and Zachary G. Nicolaou and Kathleen Champion and Jean-Christophe Loiseau and J. Nathan Kutz and Steven L. Brunton},
-    title = {PySINDy: A comprehensive Python package for robust sparse system identification},
-    journal = {Journal of Open Source Software}
-    }
-
-
-References
-----------------------
--  de Silva, Brian M., Kathleen Champion, Markus Quade,
-   Jean-Christophe Loiseau, J. Nathan Kutz, and Steven L. Brunton.
-   *PySINDy: a Python package for the sparse identification of
-   nonlinear dynamics from data.* arXiv preprint arXiv:2004.08424 (2020)
-   `[arXiv] <https://arxiv.org/abs/2004.08424>`__
-
--  Kaptanoglu, Alan A., Brian M. de Silva, Urban Fasel, Kadierdan Kaheman, Andy J. Goldschmidt
-   Jared L. Callaham, Charles B. Delahunt, Zachary G. Nicolaou, Kathleen Champion,
-   Jean-Christophe Loiseau, J. Nathan Kutz, and Steven L. Brunton.
-   *PySINDy: A comprehensive Python package for robust sparse system identification.*
-   arXiv preprint arXiv:2111.08481 (2021).
-   `[arXiv] <https://arxiv.org/abs/2111.08481>`__
-
--  Brunton, Steven L., Joshua L. Proctor, and J. Nathan Kutz.
-   *Discovering governing equations from data by sparse identification
-   of nonlinear dynamical systems.* Proceedings of the National
-   Academy of Sciences 113.15 (2016): 3932-3937.
-   `[DOI] <http://dx.doi.org/10.1073/pnas.1517384113>`__
-
--  Champion, K., Zheng, P., Aravkin, A. Y., Brunton, S. L., & Kutz, J. N. (2020).
-   *A unified sparse optimization framework to learn parsimonious physics-informed
-   models from data.* IEEE Access, 8, 169259-169271.
-   `[DOI] <https://doi.org/10.1109/ACCESS.2020.3023625>`__
-
--  Brunton, Steven L., Joshua L. Proctor, and J. Nathan Kutz.
-   *Sparse identification of nonlinear dynamics with control (SINDYc).*
-   IFAC-PapersOnLine 49.18 (2016): 710-715.
-   `[DOI] <https://doi.org/10.1016/j.ifacol.2016.10.249>`__
-
--  Kaheman, K., Kutz, J. N., & Brunton, S. L. (2020).
-   *SINDy-PI: a robust algorithm for parallel implicit sparse identification
-   of nonlinear dynamics.* Proceedings of the Royal Society A, 476(2242), 20200279.
-   `[DOI] <https://doi.org/10.1098/rspa.2020.0279>`__
-
--  Kaptanoglu, A. A., Callaham, J. L., Aravkin, A., Hansen, C. J., & Brunton, S. L. (2021).
-   *Promoting global stability in data-driven models of quadratic nonlinear dynamics.*
-   Physical Review Fluids, 6(9), 094401.
-   `[DOI] <https://doi.org/10.1103/PhysRevFluids.6.094401>`__
+-----------------
+See `Academic use <https://pysindy.readthedocs.io/en/latest/academic.html>`_.
 
 
 Related packages
@@ -232,20 +149,8 @@ Related packages
 * `PySINDyGUI <https://github.com/hyumo/pysindy-gui>`_ - A slick-looking GUI for PySINDy.
 * `SEED <https://github.com/M-Vause/SEED2.0>`_ - Software for the Extraction of Equations from Data: a GUI for many of the methods provided by PySINDy.
 * `SymINDy <https://github.com/andreikitaitsev/SymINDy/>`_ - A Python package combining SINDy with genetic programming-based symbolic regression, used for the functions library optimization.
+* `derivative <https://derivative.readthedocs.io/en/latest/>`_ - A Python package for differentiating (and optionally, smoothing) data, used by pysindy
 
-Contributors
-------------
-This repository is a fork from `original work <https://github.com/Ohjeah/sparsereg>`_ by `Markus Quade <https://github.com/Ohjeah>`_.
-
-Thanks to the members of the community who have contributed to PySINDy!
-
-+-------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| `billtubbs <https://github.com/kopytjuk>`_            | Bug fix `#68 <https://github.com/dynamicslab/pysindy/issues/68>`_                                                                                          |
-+-------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| `kopytjuk <https://github.com/kopytjuk>`_             | Concatenation feature for libraries `#72 <https://github.com/dynamicslab/pysindy/pull/72>`_                                                                |
-+-------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| `andgoldschmidt <https://github.com/andgoldschmidt>`_ | `derivative <https://derivative.readthedocs.io/en/latest/>`_ package for numerical differentiation `#85 <https://github.com/dynamicslab/pysindy/pull/85>`_ |
-+-------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+
 
 .. |BuildCI| image:: https://github.com/dynamicslab/pysindy/actions/workflows/main.yml/badge.svg
     :target: https://github.com/dynamicslab/pysindy/actions/workflows/main.yml?query=branch%3Amaster

citation.cff

@@ -0,0 +1,33 @@
+cff-version: 1.2.0
+title: pysindy
+message: >-
+  If you use this software, please cite it using the
+  metadata from this file.
+type: software
+authors:
+  - given-names: Alan
+    family-names: Kaptanoglu
+    email: [email protected]
+    affiliation: New York University
+  - given-names: Jacob
+    family-names: Stevens-Haas
+    email: [email protected]
+    affiliation: University of Washington
+    orcid: 'https://orcid.org/0000-0003-4142-5550'
+  - given-names: Kathleen
+    family-names: Champion
+    email: [email protected]
+  - given-names: Brian
+    family-names: de Silva
+    email: [email protected]
+  - given-names: Markus
+    family-names: Quade
+    email: [email protected]
+repository-code: 'https://github.com/dynamicslab/pysindy'
+url: 'https://pysindy.readthedocs.org/'
+abstract: >-
+  PySINDy is a sparse regression package with several
+  implementations for the Sparse Identification of Nonlinear
+  Dynamical systems (SINDy) method introduced in Brunton et
+  al. (2016).
+license: MIT