Metadata-Version: 2.1
Name: poissongeometry
Version: 0.1.3
Summary: A Python module for (local) Poisson-Nijenhuis calculus on Poisson manifolds
Home-page: https://github.com/appliedgeometry/poisson_geometry
Author: Miguel Evangelista-Alvarado, Jose Ruíz, Pablo Suárez-Serrato
Author-email: miguel.eva.alv@gmail.com, jcpanta@im.unam.mx, pablo@im.unam.mx
License: MIT
Description: 
        # `PoissonGeometry`
        A Python module for (local) Poisson-Nijenhuis calculus on Poisson manifolds, with the following functions:
        
        | **poisson_bracket**       | **hamiltonian_vf**            | **lichnerowicz_poisson_operator** |
        |:-------------------------:|:-----------------------------:|:---------------------------------:|
        | **modular_vf**            | **curl_operator**             | **flaschka_ratiu_bivector**       |
        | **sharp_morphism**        | **bivector_to_matrix**        | **jacobiator**                    |
        | **one_forms_bracket**     | **gauge_transformation**      | **is_homogeneous_unimodular**     |
        | **linear_normal_form_R3** | **isomorphic_lie_poisson_R3** | **is_in_kernel**                  |
        | **is_poisson_tensor**     | **is_casimir**                | **is_poisson_vf**                 | 
        |                           | **is_poisson_pair**           |                                   |
        
        This repository accompanies our paper ['On Computational Poisson Geometry I: Symbolic Foundations'](https://arxiv.org/abs/1912.01746).
        
        <!-- For more information you can read the [wiki](https://github.com/mevangelista-alvarado/poisson_geometry/wiki) this project. or the our [documentation]()-->
        
        ## Motivation
        Some of the functions in this module have been used to obtain the results in these articles:
        
         * L.  C.  Garcia-Naranjo,  P.  Suárez-Serrato & R.  Vera, <br/>
         [Poisson Structures on Smooth 4-Manifolds](https://link.springer.com/article/10.1007/s11005-015-0792-8), <br/> 
           Lett. Math. Phys. 105, 1533-1550 (2015)
           
         * P. Suárez-Serrato & J. Torres-Orozco, <br/>
         [Poisson Structures on Wrinkled Fibrations](https://link.springer.com/article/10.1007/s40590-015-0072-8),  <br/>
           Bol. Soc.Mat. Mex. 22, 263-280 (2016)
           
         * P. Suárez-Serrato, J. Torres Orozco, & R. Vera, <br/>
         [Poisson and Near-Symplectic Structures on Generalized Wrinkled Fibrations in Dimension 6](https://link.springer.com/article/10.1007/s10455-019-09651-2),  <br/>
           Ann. Glob. Anal. Geom. (2019) 55, 777-804 (2019)
           
         * M. Evangelista-Alvarado, P. Suárez-Serrato, J. Torres-Orozco & R. Vera, <br/>
         [On Bott-Morse Foliations and their Poisson Structures in Dimension 3](http://journalofsingularities.org/volume19/article2.html),  <br/>
           Journal of Singularities 19, 19-33 (2019)
        
        ## 🚀
        <!--- #### Testing: --->
         * __Run our tutorial on Colab__ [English](https://colab.research.google.com/drive/1XYcaJQ29XwkblXQOYumT1s8_00bHUEKZ) / [Castellano](https://colab.research.google.com/drive/1F9I2TcrhSz0zRZSuALEWldxgw-AL6pOK)
           
         * __Run on your local machine__
           * Clone this repository on your local machine.
           * Open a terminal with the path where you clone this repository.
           * Create a virtual environment,(see this [link](https://gist.github.com/mevangelista-alvarado/8ee2fd663e7446e543fc04eacce0f303))
           * Install the requirements:
              ```
              (venv_name) C:Users/dekstop/poisson$ pip install poissongeometry
              ```
           * Open python terminal to start:
              ```
              (venv_name) C:Users/dekstop/poisson$ python
              ```
           * Import PoissonGeometry class
              ```
              >>> from poisson.poisson import PoissonGeometry
              ```	 
        <!--  * Testing the class.
        	   For example we want convert to matriz the bivector <a href="https://www.codecogs.com/eqnedit.php?latex=$$\pi=x_{3}\frac{\partial}{\partial&space;x_{1}}\wedge\frac{\partial}{\partial&space;x_{2}}&space;-&space;x_{2}\frac{\partial}{\partial&space;x_{1}}\wedge\frac{\partial}{\partial&space;x_{3}}&space;&plus;&space;x_{1}\frac{\partial}{\partial&space;x_{2}}\wedge\frac{\partial}{\partial&space;x_{3}}$$" target="_blank"><img src="https://latex.codecogs.com/gif.latex?$$\pi=x_{3}\frac{\partial}{\partial&space;x_{1}}\wedge\frac{\partial}{\partial&space;x_{2}}&space;-&space;x_{2}\frac{\partial}{\partial&space;x_{1}}\wedge\frac{\partial}{\partial&space;x_{3}}&space;&plus;&space;x_{1}\frac{\partial}{\partial&space;x_{2}}\wedge\frac{\partial}{\partial&space;x_{3}}$$" title="$$\pi=x_{3}\frac{\partial}{\partial x_{1}}\wedge\frac{\partial}{\partial x_{2}} - x_{2}\frac{\partial}{\partial x_{1}}\wedge\frac{\partial}{\partial x_{3}} + x_{1}\frac{\partial}{\partial x_{2}}\wedge\frac{\partial}{\partial x_{3}}$$" /></a>
        	   then <a href="https://www.codecogs.com/eqnedit.php?latex=\pi" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\pi" title="\pi" /></a> is equivalent to ```{(1,2): 'x3', (1,3): '-x2', (2,3): 'x1'}``` in this class.
        	   ```
        	   >>> from poisson import PoissonGeometry
        	   >>> # We instantiate the Poisson class for dimension 3
        	   >>> pg = PoissonGeometry(3)
        	   >>> pg.bivector_to_matrix({(1,2): 'x3', (1,3): '-x2', (2,3): 'x1'})
        	   Matrix([
        	   [  0,  x3, -x2],
        	   [-x3,   0,  x1],
        	   [ x2, -x1,   0]])
        	   ```
        		
        		This function has an option for output is in latex format string, for this, we change the flag latex_format to True (its default value is False) as shown below.
        		
        		```
        		 >>> print(pg.bivector_to_matrix({(1,2): 'x3', (1,3): '-x2', (2,3): 'x1'}, latex_format=True))
        		   \left[\begin{array}{ccc}0 & x_{3} & - x_{2}\\- x_{3} & 0 & x_{1}\\x_{2} & - x_{1} & 0\end{array}\right]
        		```
        		<!--For more information to how use this class you can read the [documentation]() or the our [wiki](https://github.com/mevangelista-alvarado/poisson_geometry/wiki)-->
        
        <!--## TO DO
        Calculate Poisson Cohomology with linear coefficients.-->
        
        ## Bugs & Contributions
        Our issue tracker is at https://github.com/appliedgeometry/poissongeometry/issues. Please report any bugs that you find. Or, even better, if you are interested in our project you can fork the repository on GitHub and create a pull request. 
        
        ## Licence 📄
        MIT licence
        
        ## Authors ✒️
        This work is developed and maintained by:
         * **Miguel Evangelista Alvarado** - [@mevangelista-alvarado](https://github.com/mevangelista-alvarado)
         * **Jose Ruíz Pantaleón** - [@jcrpanta](https://github.com/jcrpanta)
         * **Pablo Suárez Serrato** - [@psuarezserrato](https://github.com/psuarezserrato)
        
        ## Thanks for citing our work if you use it! 🤓 ##
        
        @misc{evangelistaalvarado2019computational,<br/>
            title={On Computational Poisson Geometry I: Symbolic Foundations},<br/>
            author={M. A. Evangelista-Alvarado and J. C. Ruíz-Pantaleón and P. Suárez-Serrato},<br/>
            year={2019},<br/>
            eprint={1912.01746},<br/>
            archivePrefix={arXiv},<br/>
            primaryClass={math.DG}<br/>
        }
        
        ## Acknowledgments ##
        This work was partially supported by the grants “Programa para un Avance Global e Integrado de la Matemática Mexicana” CONACyT FORDECYT 26566 and "Aprendizaje Geométrico Profundo" UNAM-DGAPA-PAPIIT-IN104819.
        
        <!-- 
        ## Do not forget.
        * Comment to others about this project 📢
        * Cite this project if you use it 🤓.
        * Finally, if you know one of the authors, invite him a beer🍺.
        ---
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
