Skip to main content

Python binding for cvodes from the sundials library.

Project description

Build status on private Drone server Build status on CircleCI Build status on Travis-CI PyPI version License coverage https://zenodo.org/badge/43224425.svg

pycvodes provides a Python binding to the Ordinary Differential Equation integration routines from cvodes in the SUNDIALS suite. pycvodes allows a user to numerically integrate (systems of) differential equations. Note that routines for sensitivity analysis is not yet exposed in this binding (which makes the functionality essentially the same as cvode).

The following multistep methods are available:

  • bdf: Backward differentiation formula (of order 1 to 5)
  • adams: implicit Adams method (order 1 to 12)

Note that bdf (as an implicit stepper) requires a user supplied callback for calculating the jacobian.

You may also want to know that you can use pycvodes from pyodesys which can e.g. derive the Jacobian analytically (using SymPy). Pyodesys also provides plotting functions, C++ code-generation and more.

Documentation

Autogenerated API documentation for latest stable release is found here: https://bjodah.github.io/pycvodes/latest (and the development version for the current master branch are found here: http://hera.physchem.kth.se/~pycvodes/branches/master/html).

Installation

Simplest way to install is to use the conda package manager:

$ conda install -c conda-forge pycvodes pytest
$ python -m pytest --pyargs pycvodes

tests should pass.

Manual installation

Binary distribution is available here: https://anaconda.org/bjodah/pycvodes

Source distribution is available here: https://pypi.python.org/pypi/pycvodes

When installing from source you can choose what lapack lib to link against by setting the environment variable PYCVODES_LAPACK, your choice can later be accessed from python:

>>> from pycvodes import _config
>>> _config.env['LAPACK']  # doctest: +SKIP
'lapack,blas'

If you use pip to install pycvodes note that you will need to install sundials (and its development headers, with cvodes & lapack enabled) prior to installing pycvodes.

Examples

The classic van der Pol oscillator (see examples/van_der_pol.py)

>>> import numpy as np
>>> from pycvodes import integrate_predefined  # also: integrate_adaptive
>>> mu = 1.0
>>> def f(t, y, dydt):
...     dydt[0] = y[1]
...     dydt[1] = -y[0] + mu*y[1]*(1 - y[0]**2)
...
>>> def j(t, y, Jmat, dfdt=None, fy=None):
...     Jmat[0, 0] = 0
...     Jmat[0, 1] = 1
...     Jmat[1, 0] = -1 - mu*2*y[1]*y[0]
...     Jmat[1, 1] = mu*(1 - y[0]**2)
...     if dfdt is not None:
...         dfdt[:] = 0
...
>>> y0 = [1, 0]; dt0=1e-8; t0=0.0; atol=1e-8; rtol=1e-8
>>> tout = np.linspace(0, 10.0, 200)
>>> yout, info = integrate_predefined(f, j, y0, tout, atol, rtol, dt0,
...                                   method='bdf')
>>> import matplotlib.pyplot as plt
>>> series = plt.plot(tout, yout)
>>> plt.show()  # doctest: +SKIP
https://raw.githubusercontent.com/bjodah/pycvodes/master/examples/van_der_pol.png

For more examples see examples/, and rendered jupyter notebooks here: http://hera.physchem.kth.se/~pycvodes/branches/master/examples

License

The source code is Open Source and is released under the simplified 2-clause BSD license. See LICENSE for further details.

Contributors are welcome to suggest improvements at https://github.com/bjodah/pycvodes

Author

Björn I. Dahlgren, contact:

  • gmail address: bjodah

See file AUTHORS in root for a list of all authors.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
pycvodes-0.11.9.tar.gz (298.7 kB) Copy SHA256 hash SHA256 Source None Oct 11, 2018

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page