Metadata-Version: 2.1
Name: py-pde
Version: 0.2
Summary: Python package for solving partial differential equations
Home-page: https://github.com/zwicker-group/py-pde
Author: David Zwicker
Author-email: david.zwicker@ds.mpg.de
License: MIT
Download-URL: https://github.com/zwicker-group/py-pde/archive/v0.2.tar.gz
Description: # py-pde
        
        [![Build Status](https://travis-ci.org/zwicker-group/py-pde.svg?branch=master)](https://travis-ci.org/zwicker-group/py-pde)
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        `py-pde` is a Python package for solving partial differential equations (PDEs). 
        The package provides classes for scalar and tensor fields discretized on grids
        as well as associated differential operators.
        This allows defining, inspecting, and solving typical PDEs that appear for
        instance in the study of dynamical systems in physics.
        The focus of the package lies on easy usage to explore the behavior of PDEs.
        However, core computations can be compiled transparently using numba for speed.
        
        [Try it out online!](https://mybinder.org/v2/gh/zwicker-group/py-pde/master?filepath=examples%2Fjupyter)
        
        
        Installation
        ------------
        
        `py-pde` is available on `pypi`, so you should be able to install it through
        `pip`:
        
        ```bash
        pip install py-pde
        ```
        
        In order to have all features of the package available, you might also want to 
        install the following optional packages:
        
        ```bash
        pip install h5py pandas tqdm
        ```
        
        Moreover, `ffmpeg` needs to be installed and for creating movies.
        
        
        Usage
        -----
        
        A simple example showing the evolution of the diffusion equation in 2d:
        
        ```python
        from pde.common import *
        
        grid = UnitGrid([64, 64])                 # generate grid
        state = ScalarField.random_uniform(grid)  # generate initial condition
        
        eq = DiffusionPDE(diffusivity=0.1)        # define the pde
        result = eq.solve(state, t_range=10)      # solve the pde
        result.plot()                             # plot the resulting field
        ```
        
        More examples illustrating the capabilities of the package can be found in the
         `examples` folder.
        A detailed [documentation is available on readthedocs](https://py-pde.readthedocs.io/)
        and as [a single PDF file](https://py-pde.readthedocs.io/_/downloads/en/latest/pdf/).
        
        
Keywords: pdes,partial-differential-equations,dynamical-systems
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown
