Metadata-Version: 2.1
Name: leibniz
Version: 0.0.23
Summary: Leibniz is a package providing facilities to express learnable differential equations based on PyTorch
Home-page: https://github.com/caiyunapp/leibniz
Author: Mingli Yuan
Author-email: mingli.yuan@gmail.com
License: UNKNOWN
Project-URL: Documentation, https://packaging.python.org/tutorials/distributing-packages/
Project-URL: Source, https://github.com/caiyunapp/leibniz
Project-URL: Tracker, https://github.com/caiyunapp/leibniz/issues
Description: # Leibniz
        
        [![Build Status](https://api.travis-ci.com/caiyunapp/leibniz.svg?branch=master)](http://travis-ci.com/caiyunapp/leibniz) 
        
        Leibniz is a python package which provide facilities to express learnable differential equations with PyTorch
        
        
        Install
        --------
        
        ```bash
        pip install leibniz
        ```
        
        
        How to use
        -----------
        
        As an example we solve a very simple advection problem, a box-shaped material transported by a constant steady wind.
        
        ![moving box](https://raw.githubusercontent.com/caiyunapp/leibniz/master/advection_3d.gif)
        
        
        ```python
        import torch as th
        import leibniz as lbnz
        
        from leibniz.core3d.gridsys.regular3 import RegularGrid
        from leibniz.diffeq import odeint as odeint
        
        
        def binary(tensor):
            return th.where(tensor > lbnz.zero, lbnz.one, lbnz.zero)
        
        # setup grid system
        lbnz.bind(RegularGrid(
            basis='x,y,z',
            W=51, L=151, H=51,
            east=16.0, west=1.0,
            north=6.0, south=1.0,
            upper=6.0, lower=1.0
        ))
        lbnz.use('x,y,z') # use xyz coordinate
        
        # giving a material field as a box 
        fld = binary((lbnz.x - 8) * (9 - lbnz.x)) * \
              binary((lbnz.y - 3) * (4 - lbnz.y)) * \
              binary((lbnz.z - 3) * (4 - lbnz.z))
        
        # construct a constant steady wind
        wind = lbnz.one, lbnz.zero, lbnz.zero
        
        # transport value by wind
        def derivitive(t, clouds):
            return - lbnz.upwind(wind, clouds)
        
        # integrate the system with rk4
        pred = odeint(derivitive, fld, th.arange(0, 7, 1 / 100), method='rk4')
        ```
        
        How to release
        ---------------
        
        ```bash
        python3 setup.py sdist bdist_wheel
        python3 -m twine upload dist/*
        
        git tag va.b.c master
        git push origin va.b.c
        ```
        
        Contributors
        ------------
        
        * Mingli Yuan ([Mountain](https://github.com/mountain))
        * Xiang Pan ([Panpanx](https://github.com/Panpanx))
        
        Acknowledge
        -----------
        
        We included source code with minor changes from [torchdiffeq](https://github.com/rtqichen/torchdiffeq) by Ricky Chen,
        because of two purpose:
        1. package torchdiffeq is not indexed by pypi
        2. package torchdiffeq is very convenient and mandatory
        
        All our contribution is based on Ricky's Neural ODE paper (NIPS 2018) and his package.
        
         
        
Platform: UNKNOWN
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
